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Download full pdf book Deep Learning with Python by Francois Chollet available in full 384 pages, and make sure to check out other latest books Computers related to Deep Learning with Python below.Deep Learning with Python
By Francois Chollet ISBN Code: : 1638352046
 Publisher : Simon and Schuster
 Pages : 384
 Category : Computers
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 Pdf : deeplearningwithpython.pdf
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Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from nearunusable speech and image recognition, to nearhuman accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, naturallanguage processing, and generative models. By the time you finish, you'll have the knowledge and handson skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deeplearning environment Imageclassification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deeplearning library, as well as a contributor to the TensorFlow machinelearning framework. He also does deeplearning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1  FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2  DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deeplearning best practices Generative deep learning Conclusions appendix A  Installing Keras and its dependencies on Ubuntu appendix B  Running Jupyter notebooks on an EC2 GPU instance
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Python Programming
By Frank Millstein File : Pdf, ePub, Mobi, Kindle
 Publisher : Frank Millstein
 Book Code : N.a
 Total of Pages : 642
 Category : Computers
 Members : 316
 Pdf File: pythonprogramming.pdf
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Programming With Python  8 BOOK BUNDLE!! Deep Learning With Keras Here Is A Preview Of What You’ll Learn Here… The difference between deep learning and machine learning Deep neural networks Convolutional neural networks Building deep learning models with Keras Multilayer perceptron network models And much more... Convolutional Neural Networks In Python Here Is A Preview Of What You’ll Learn Here… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator How to build a simple image classification CNN And much, much more! Python Machine Learning Here Is A Preview Of What You’ll Learn Here… Basics behind machine learning techniques Most commonly used machine learning algorithms, linear and logistic regression, decision trees support vector machines, knearest neighbors, random forests Solving multiclasisfication problems Data visualization with Matplotlib and data transformation with Pandas and Scikitlearn Solving multilabel classification problems And much, much more... Machine Learning With TensorFlow Here Is A Preview Of What You’ll Learn Here… What is machine learning Main uses and benefits of machine learning How to get started with TensorFlow, installing and loading data Data flow graphs and basic TensorFlow expressions Creating MNIST classifiers with onehot transformation And much, much more... Data Analytics With Python Here Is A Preview Of What You’ll Learn Here… What is Data Analytics? Difference between data science, big data and data analytics Installing python Python data structures Pandas series and data frames And much, much more... Natural Language Processing With Python Here Is A Preview Of What You’ll Learn Here… Challenges of natural language processing How natural language processing works? Part of speech tagging Ngrams Running natural language processing script And much, much more... DevOps Handbook Here Is A Preview Of What You’ll Learn Here… Issues and mistakes plaguing software development What is software development life cycle? How software development life cycle works? The origins of devops Testing and building systems tools And much, much more... DevOps Adoption Here Is A Preview Of What You’ll Learn Here… Devops definition Overcoming traditional dev and ops Devops and security integration Devops success factors Is devops right for you? And much, much more... Get this book bundle NOW and SAVE money!
Python Machine Learning
By Sebastian Raschka File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1783555149
 Total of Pages : 454
 Category : Computers
 Members : 214
 Pdf File: pythonmachinelearning.pdf
Book Short Summary:
Unlock deeper insights into Machine Leaning with this vital guide to cuttingedge predictive analytics About This Book Leverage Python's most powerful opensource libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective preprocessing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikitlearn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
HandsOn Transfer Learning with Python
By Dipanjan Sarkar,Raghav Bali,Tamoghna Ghosh File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1788839056
 Total of Pages : 438
 Category : Computers
 Members : 694
 Pdf File: handsontransferlearningwithpython.pdf
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Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve realworld research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is twofold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easytofollow concepts and examples. The second area of focus is realworld examples and research problems using TensorFlow, Keras, and the Python ecosystem with handson examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long shortterm memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, finetuning, pretrained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of realworld case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore realworld research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for HandsOn Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying stateoftheart transfer learning methodologies to solve tough realworld problems. Basic proficiency in machine learning and Python is required.
Deep Learning with PyTorch
By Luca Pietro Giovanni Antiga,Eli Stevens,Thomas Viehmann File : Pdf, ePub, Mobi, Kindle
 Publisher : Simon and Schuster
 Book Code : 1638354073
 Total of Pages : 520
 Category : Computers
 Members : 837
 Pdf File: deeplearningwithpytorch.pdf
Book Short Summary:
“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, cocreator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikitlearn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is cofounder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1  CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Realworld data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2  LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 Endtoend nodule analysis, and where to go next PART 3  DEPLOYMENT 15 Deploying to production
Deep Learning with R
By J.J. Allaire File : Pdf, ePub, Mobi, Kindle
 Publisher : Simon and Schuster
 Book Code : 1638351635
 Total of Pages : 360
 Category : Computers
 Members : 707
 Pdf File: deeplearningwithr.pdf
Book Short Summary:
Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Continue your journey into the world of deep learning with Deep Learning with R in Motion, a practical, handson video course available exclusively at Manning.com (www.manning.com/livevideo/deeplearningwithrinmotion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. Deeplearning systems now enable previously impossible smart applications, revolutionizing image recognition and naturallanguage processing, and identifying complex patterns in data. The Keras deeplearning library provides data scientists and developers working in R a stateoftheart toolset for tackling deeplearning tasks. About the Book Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with Rbased applications in computer vision, naturallanguage processing, and generative models. What's Inside Deep learning from first principles Setting up your own deeplearning environment Image classification and generation Deep learning for text and sequences About the Reader You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed. About the Authors François Chollet is a deeplearning researcher at Google and the author of the Keras library. J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras. Table of Contents PART 1  FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2  DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deeplearning best practices Generative deep learning Conclusions
Pragmatic Machine Learning with Python
By Avishek Nag File : Pdf, ePub, Mobi, Kindle
 Publisher : BPB Publications
 Book Code : 938984536X
 Total of Pages : 340
 Category : Computers
 Members : 759
 Pdf File: pragmaticmachinelearningwithpython.pdf
Book Short Summary:
An easytounderstand guide to learn practical Machine Learning techniques with Mathematical foundations KEY FEATURES  A balanced combination of underlying mathematical theories & practical examples with Python code  Coverage of latest topics like multilabel classification, Text Mining, Doc2Vec, Word2Vec, XMeans clustering, unsupervised outlier detection, techniques to deploy ML models in productiongrade systems with PMML, etc  Coverage of sufficient & relevant visualization techniques specific to any topic DESCRIPTION This book will be ideal for working professionals who want to learn Machine Learning from scratch. The first chapter will be an introductory chapter to make readers comfortable with the idea of Machine Learning and the required mathematical theories. There will be a balanced combination of underlying mathematical theories corresponding to any Machine Learning topic and its implementation using Python. Most of the implementations will be based on ‘scikitlearn,’ but other Python libraries like ‘Gensim’ or ‘PyTorch’ will also be used for some topics like text analytics or deep learning. The book will be divided into chapters based on primary Machine Learning topics like Classification, Regression, Clustering, Deep Learning, Text Mining, etc. The book will also explain different techniques of putting Machine Learning models into productiongrade systems using Big Data or NonBig Data flavors and standards for exporting models. WHAT WILL YOU LEARN  Get familiar with practical concepts of Machine Learning from ground zero  Learn how to deploy Machine Learning models in production  Understand how to do “Data Science Storytelling”  Explore the latest topics in the current industry about Machine Learning WHO THIS BOOK IS FOR This book would be ideal for experienced Software Professionals who are trying to get into the field of Machine Learning. Anyone who wishes to Learn Machine Learning concepts and models in the production lifecycle. TABLE OF CONTENTS 1. Introduction to Machine Learning & Mathematical preliminaries 2. Classification 3. Regression 4. Clustering 5. Deep Learning & Neural Networks 6. Miscellaneous Unsupervised Learning 7. Text Mining 8. Machine Learning models in production 9. Case Studies & Data Science Storytelling
Building Machine Learning Systems with Python  Second Edition
By Luis Pedro Coelho,Willi Richert File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 178439288X
 Total of Pages : 326
 Category : Computers
 Members : 430
 Pdf File: buildingmachinelearningsystemswithpythonsecondedition.pdf
Book Short Summary:
This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.
Neural Network Projects with Python
By James Loy File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1789133319
 Total of Pages : 308
 Category : Computers
 Members : 722
 Pdf File: neuralnetworkprojectswithpython.pdf
Book Short Summary:
Build your Machine Learning portfolio by creating 6 cuttingedge Artificial Intelligence projects using neural networks in Python Key FeaturesDiscover neural network architectures (like CNN and LSTM) that are driving recent advancements in AIBuild expert neural networks in Python using popular libraries such as KerasIncludes projects such as object detection, face identification, sentiment analysis, and moreBook Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many realworld problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain handson experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cuttingedge AI projects in Python that will immediately strengthen your machine learning portfolio. What you will learnLearn various neural network architectures and its advancements in AIMaster deep learning in Python by building and training neural networkMaster neural networks for regression and classificationDiscover convolutional neural networks for image recognitionLearn sentiment analysis on textual data using Long ShortTerm MemoryBuild and train a highly accurate facial recognition security systemWho this book is for This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.
Introduction to Machine Learning with Python
By Andreas C. Müller,Sarah Guido File : Pdf, ePub, Mobi, Kindle
 Publisher : "O'Reilly Media, Inc."
 Book Code : 1449369898
 Total of Pages : 400
 Category : Computers
 Members : 348
 Pdf File: introductiontomachinelearningwithpython.pdf
Book Short Summary:
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machinelearning application with Python and the scikitlearn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including textspecific processing techniques Suggestions for improving your machine learning and data science skills
Deep Learning with Keras
By Antonio Gulli,Sujit Pal File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1787129039
 Total of Pages : 318
 Category : Computers
 Members : 191
 Pdf File: deeplearningwithkeras.pdf
Book Short Summary:
Get to grips with the basics of Keras to implement fast and efficient deeplearning models About This Book Implement various deeplearning algorithms in Keras and see how deeplearning can be used in games See how various deeplearning models and practical usecases can be implemented using Keras A practical, handson guide with realworld examples to give you a strong foundation in Keras Who This Book Is For If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deeplearning with Keras. A knowledge of Python is required for this book. What You Will Learn Optimize stepbystep functions on a large neural network using the Backpropagation Algorithm Finetune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore nontraditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. Style and approach This book is an easytofollow guide full of examples and realworld applications to help you gain an indepth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras.
Introduction to Deep Learning and Neural Networks with PythonTM
By Ahmed Fawzy Gad,Fatima Ezzahra Jarmouni File : Pdf, ePub, Mobi, Kindle
 Publisher : Academic Press
 Book Code : 0323909345
 Total of Pages : 300
 Category : Medical
 Members : 612
 Pdf File: introductiontodeeplearningandneuralnetworkswithpythontm.pdf
Book Short Summary:
Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive stepbystep guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book’s end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. Examines the practical side of deep learning and neural networks Provides a problembased approach to building artificial neural networks using real data Describes PythonTM functions and features for neuroscientists Uses a careful tutorial approach to describe implementation of neural networks in PythonTM Features math and code examples (via companion website) with helpful instructions for easy implementation
Applied Artificial Intelligence
By Wolfgang Beer File : Pdf, ePub, Mobi, Kindle
 Publisher : Wolfgang Beer
 Book Code : N.a
 Total of Pages :
 Category : Computers
 Members : 683
 Pdf File: appliedartificialintelligence.pdf
Book Short Summary:
About This Book Step into the amazing world of Artificial Intelligence and Machine Learning using this compact and easy to understand book. Dive into Neural Networks and Deep Learning and create your own production ready AI models by using TensorFlow and Keras. Work through simple yet insightful examples that will get you up and running with Artificial Intelligence, TensorFlow and Keras in no time. Who This Book Is For This book is for Python developers who want to understand Neural Networks from ground up and build realworld Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. What You Will Learn The basic structure and functionality of a Neuron The basic math behind the Neural Network learning process See how to build a simple character recognition model from ground up What classification, regression and clustering is How to use TensorFlow to build production ready models Build a first model with the Keras framework How to predict the survival chance for Titanic passengers How to build a simple book recommender How to detect toxic language with an AI model In Detail Artificial Intelligence became one of the hottest topics in the modern economy, where everything is driven by software, network and data. There exists nearly no startup nor traditional business where Artificial Intelligence is not used extensively across many fields such as search engines, image recognition, robotics or finance. This book gives a ground up, step by step introduction about how a Neural Network is used to learn a given function and to make intelligent datadriven decisions. The book explains how to identify typical usecases such as classification, regression and clustering in terms of practical and well known usecases. This book comes with an introduction into the stateoftheart Google TensorFlow framework that allows developers to roll out their models in production. On top of TensorFlow, the Keras library is used to simplify the design and training of complex deeplearning models. This book comes with multiple examples that show how to apply Artificial Intelligence and Machine Learning models for usecases such as handwriting recognition, decision making, text analysis and toxic comment identification as well as the use of AI to recommend products to customers.
Machine Learning for TimeSeries with Python
By Ben Auffarth File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1801816107
 Total of Pages : 370
 Category : Computers
 Members : 766
 Pdf File: machinelearningfortimeserieswithpython.pdf
Book Short Summary:
Get better insights from timeseries data and become proficient in model performance analysis Key FeaturesExplore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time series via realworld case studies on operations management, digital marketing, finance, and healthcareBook Description The Python timeseries ecosystem is huge and often quite hard to get a good grasp on, especially for timeseries since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python timeseries packages and help you build better predictive systems. Machine Learning for TimeSeries with Python starts by reintroducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern nonparametric models. By observing practical examples and the theory behind them, you will become confident with loading timeseries datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You'll also have a look at realworld case studies covering weather, traffic, biking, and stock market data. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to timeseries. What you will learnUnderstand the main classes of time series and learn how to detect outliers and patternsChoose the right method to solve timeseries problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with timeseries data visualizationUnderstand classical timeseries models like ARMA and ARIMAImplement deep learning models, like Gaussian processes, transformers, and stateoftheart machine learning modelsBecome familiar with many libraries like Prophet, XGboost, and TensorFlowWho this book is for This book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.
Recurrent Neural Networks with Python Quick Start Guide
By Simeon Kostadinov File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1789133661
 Total of Pages : 122
 Category : Computers
 Members : 808
 Pdf File: recurrentneuralnetworkswithpythonquickstartguide.pdf
Book Short Summary:
Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework. Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow libraryApply long shortterm memory unitsExpand your skills in complex neural network and deep learning topicsBook Description Developers struggle to find an easytofollow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the stateoftheart model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and realworld implementations of concepts to build a solid foundation of neural network modeling. Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical stateoftheart RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field. What you will learnUse TensorFlow to build RNN modelsUse the correct RNN architecture for a particular machine learning taskCollect and clear the training data for your modelsUse the correct Python libraries for any task during the building phase of your modelOptimize your model for higher accuracyIdentify the differences between multiple models and how you can substitute themLearn the core deep learning fundamentals applicable to any machine learning modelWho this book is for This book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical usecases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.
Python Machine Learning By Example
By Yuxi (Hayden) Liu File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1800203861
 Total of Pages : 526
 Category : Computers
 Members : 186
 Pdf File: pythonmachinelearningbyexample.pdf
Book Short Summary:
Equipped with the latest updates, this third edition of Python Machine Learning By Example provides a comprehensive course for ML enthusiasts to strengthen their command of ML concepts, techniques, and algorithms.
Interpretable Machine Learning with Python
By Serg Masís File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1800206577
 Total of Pages : 736
 Category : Computers
 Members : 892
 Pdf File: interpretablemachinelearningwithpython.pdf
Book Short Summary:
Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models Key FeaturesLearn how to extract easytounderstand insights from any machine learning modelBecome wellversed with interpretability techniques to build fairer, safer, and more reliable modelsMitigate risks in AI systems before they have broader implications by learning how to debug blackbox modelsBook Description Do you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models. The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how whitebox models work, compare them to blackbox and glassbox models, and examine their tradeoff. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, timeseries, image or text. In addition to the stepbystep code, the book also helps the reader to interpret model outcomes using examples. In the third section, you’ll get handson with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from stateoftheart feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learnRecognize the importance of interpretability in businessStudy models that are intrinsically interpretable such as linear models, decision trees, and Naïve BayesBecome wellversed in interpreting models with modelagnostic methodsVisualize how an image classifier works and what it learnsUnderstand how to mitigate the influence of bias in datasetsDiscover how to make models more reliable with adversarial robustnessUse monotonic constraints to make fairer and safer modelsWho this book is for This book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.
Deep Learning from the Basics
By Koki Saitoh File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 180020972X
 Total of Pages : 316
 Category : Computers
 Members : 195
 Pdf File: deeplearningfromthebasics.pdf
Book Short Summary:
Discover ways to implement various deep learning algorithms by leveraging Python and other technologies Key FeaturesLearn deep learning models through several activitiesBegin with simple machine learning problems, and finish by building a complex system of your ownTeach your machines to see by mastering the technologies required for image recognitionBook Description Deep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us. Deep Learning from the Basics begins with a fastpaced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you'll discover backpropagation—an efficient way to calculate the gradients of weight parameters—and study multilayer perceptrons and their limitations, before, finally, implementing a threelayer neural network and calculating multidimensional arrays. By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning. What you will learnUse Python with minimum external sources to implement deep learning programsStudy the various deep learning and neural network theoriesLearn how to determine learning coefficients and the initial values of weightsImplement trends such as Batch Normalization, Dropout, and AdamExplore applications like automatic driving, image generation, and reinforcement learningWho this book is for Deep Learning from the Basics is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies. Some working knowledge of Python is a must. Knowledge of NumPy and pandas will be beneficial, but not essential.
Artificial Intelligence with Python
By Prateek Joshi File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1786469677
 Total of Pages : 446
 Category : Computers
 Members : 110
 Pdf File: artificialintelligencewithpython.pdf
Book Short Summary:
Build realworld Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build realworld Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various realworld scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to realworld scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
Machine Learning for Financial Risk Management with Python
By Abdullah Karasan File : Pdf, ePub, Mobi, Kindle
 Publisher : "O'Reilly Media, Inc."
 Book Code : 1492085200
 Total of Pages : 334
 Category : Computers
 Members : 734
 Pdf File: machinelearningforfinancialriskmanagementwithpython.pdf
Book Short Summary:
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Pythonbased machine learning and deep learning models for assessing financial risk. Building handson AIbased financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models
HandsOn Machine Learning with ScikitLearn, Keras, and TensorFlow
By Aurélien Géron File : Pdf, ePub, Mobi, Kindle
 Publisher : "O'Reilly Media, Inc."
 Book Code : 149203259X
 Total of Pages : 856
 Category : Computers
 Members : 127
 Pdf File: handsonmachinelearningwithscikitlearnkerasandtensorflow.pdf
Book Short Summary:
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two productionready Python frameworks—ScikitLearn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use ScikitLearn to track an example machinelearning project endtoend Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Machine Learning with Python for Everyone
By Mark Fenner File : Pdf, ePub, Mobi, Kindle
 Publisher : AddisonWesley Professional
 Book Code : 0134845641
 Total of Pages : 99998
 Category : Computers
 Members : 412
 Pdf File: machinelearningwithpythonforeveryone.pdf
Book Short Summary:
The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little collegelevel math you know. Principal instructor Mark E. Fenner relies on plainEnglish stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikitlearn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
HandsOn Deep Learning Algorithms with Python
By Sudharsan Ravichandiran File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1789344514
 Total of Pages : 512
 Category : Computers
 Members : 589
 Pdf File: handsondeeplearningalgorithmswithpython.pdf
Book Short Summary:
This book introduces basictoadvanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Pythonbased deep learning libraries such as TensorFlow.
Python Programming, Deep Learning
By Anthony Adams File : Pdf, ePub, Mobi, Kindle
 Publisher : Anthony Adams
 Book Code : N.a
 Total of Pages : 315
 Category : Computers
 Members : 230
 Pdf File: pythonprogrammingdeeplearning.pdf
Book Short Summary:
Easily Boost Your Skills In Python Programming & Become A Master In Deep Learning & Data Analysis! 💻 Python is an interpreted, highlevel, generalpurpose programming language that emphasizes code readability with its notable use of significant whitespace. What makes Python so popular in the IT industry is that it uses an objectoriented approach, which enables programmers to write clear, logical code for all types of projects, whether big or small. Hone your Python Programming skills and gain a sharp edge over other programmers the EASIEST way possible... with this practical beginner’s guide! In his 3in1 Python crash course for beginners, Anthony Adams gives novices like you simple, yet efficient tips and tricks to become a MASTER in Python coding for artificial intelligence, neural networks, machine learning, and data science/analysis! Here’s what you’ll get: ✅ Highly innovative ways to boost your understanding of Python programming, data analysis, and machine learning ✅ Quickly and effectively stop fraud with machine learning ✅ Practical and efficient exercises that make understanding Python quick & easy And so much more! As a beginner, you might feel a bit intimidated by the complexities of coding. Add the fact that most Python Programming crash course guides make learning harder than it has to be! ✓ With the help of this 3in1 guide, you will be given carefully sequenced Python Programming lessons that’ll maximize your understanding, and equip you with all the skills for reallife application! ★ Thrive in the IT industry with this comprehensive Python Programming crash course! ★ Scroll up, Click on “Buy Now”, and Start Learning Today!
Advanced Machine Learning with Python
By John Hearty File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1784393835
 Total of Pages : 278
 Category : Computers
 Members : 460
 Pdf File: advancedmachinelearningwithpython.pdf
Book Short Summary:
Solve challenging data science problems by mastering cuttingedge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles realworld computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or texttagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cuttingedge deep learning algorithms Apply your new found skills to solve real problems, through clearlyexplained code for every technique and test Automate large sets of complex data and overcome timeconsuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validationfocused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semisupervised learning, and more  all whilst working with realworld applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semisupervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and wellunderstood. Each topic is described with realworld applications, providing both broad contextual coverage and detailed guidance.
PYTHON GUI PROJECTS WITH MACHINE LEARNING AND DEEP LEARNING
By Vivian Siahaan,Rismon Hasiholan Sianipar File : Pdf, ePub, Mobi, Kindle
 Publisher : BALIGE PUBLISHING
 Book Code : N.a
 Total of Pages : 917
 Category : Computers
 Members : 983
 Pdf File: pythonguiprojectswithmachinelearninganddeeplearning.pdf
Book Short Summary:
PROJECT 1: THE APPLIED DATA SCIENCE WORKSHOP: Prostate Cancer Classification and Recognition Using Machine Learning and Deep Learning with Python GUI Prostate cancer is cancer that occurs in the prostate. The prostate is a small walnutshaped gland in males that produces the seminal fluid that nourishes and transports sperm. Prostate cancer is one of the most common types of cancer. Many prostate cancers grow slowly and are confined to the prostate gland, where they may not cause serious harm. However, while some types of prostate cancer grow slowly and may need minimal or even no treatment, other types are aggressive and can spread quickly. The dataset used in this project consists of 100 patients which can be used to implement the machine learning and deep learning algorithms. The dataset consists of 100 observations and 10 variables (out of which 8 numeric variables and one categorical variable and is ID) which are as follows: Id, Radius, Texture, Perimeter, Area, Smoothness, Compactness, Diagnosis Result, Symmetry, and Fractal Dimension. The models used in this project are KNearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: THE APPLIED DATA SCIENCE WORKSHOP: Urinary Biomarkers Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI Pancreatic cancer is an extremely deadly type of cancer. Once diagnosed, the fiveyear survival rate is less than 10%. However, if pancreatic cancer is caught early, the odds of surviving are much better. Unfortunately, many cases of pancreatic cancer show no symptoms until the cancer has spread throughout the body. A diagnostic test to identify people with pancreatic cancer could be enormously helpful. In a paper by Silvana Debernardi and colleagues, published this year in the journal PLOS Medicine, a multinational team of researchers sought to develop an accurate diagnostic test for the most common type of pancreatic cancer, called pancreatic ductal adenocarcinoma or PDAC. They gathered a series of biomarkers from the urine of three groups of patients: Healthy controls, Patients with noncancerous pancreatic conditions, like chronic pancreatitis, and Patients with pancreatic ductal adenocarcinoma. When possible, these patients were age and sexmatched. The goal was to develop an accurate way to identify patients with pancreatic cancer. The key features are four urinary biomarkers: creatinine, LYVE1, REG1B, and TFF1. Creatinine is a protein that is often used as an indicator of kidney function. YVLE1 is lymphatic vessel endothelial hyaluronan receptor 1, a protein that may play a role in tumor metastasis. REG1B is a protein that may be associated with pancreas regeneration. TFF1 is trefoil factor 1, which may be related to regeneration and repair of the urinary tract. The models used in this project are KNearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: DATA SCIENCE CRASH COURSE: Voice Based Gender Classification and Prediction Using Machine Learning and Deep Learning with Python GUI This dataset was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are preprocessed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz280hz (human vocal range). The following acoustic properties of each voice are measured and included within the CSV: meanfreq: mean frequency (in kHz); sd: standard deviation of frequency; median: median frequency (in kHz); Q25: first quantile (in kHz); Q75: third quantile (in kHz); IQR: interquantile range (in kHz); skew: skewness; kurt: kurtosis; sp.ent: spectral entropy; sfm: spectral flatness; mode: mode frequency; centroid: frequency centroid (see specprop); peakf: peak frequency (frequency with highest energy); meanfun: average of fundamental frequency measured across acoustic signal; minfun: minimum fundamental frequency measured across acoustic signal; maxfun: maximum fundamental frequency measured across acoustic signal; meandom: average of dominant frequency measured across acoustic signal; mindom: minimum of dominant frequency measured across acoustic signal; maxdom: maximum of dominant frequency measured across acoustic signal; dfrange: range of dominant frequency measured across acoustic signal; modindx: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range; and label: male or female. The models used in this project are KNearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: DATA SCIENCE CRASH COURSE: Thyroid Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Thyroid disease is a general term for a medical condition that keeps your thyroid from making the right amount of hormones. Thyroid typically makes hormones that keep body functioning normally. When the thyroid makes too much thyroid hormone, body uses energy too quickly. The two main types of thyroid disease are hypothyroidism and hyperthyroidism. Both conditions can be caused by other diseases that impact the way the thyroid gland works. Dataset used in this project was from Garavan Institute Documentation as given by Ross Quinlan 6 databases from the Garavan Institute in Sydney, Australia. Approximately the following for each database: 2800 training (data) instances and 972 test instances. This dataset contains plenty of missing data, while 29 or so attributes, either Boolean or continuouslyvalued. The models used in this project are KNearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Machine Learning Engineering with Python
By Andrew P. McMahon File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 180107710X
 Total of Pages : 276
 Category : Computers
 Members : 297
 Pdf File: machinelearningengineeringwithpython.pdf
Book Short Summary:
Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments Key FeaturesExplore hyperparameter optimization and model management toolsLearn objectoriented programming and functional programming in Python to build your own ML libraries and packagesExplore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use casesBook Description Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create highquality machine learning products and services. Machine Learning Engineering with Python takes a handson approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get handson with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloudbased tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build endtoend machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering. What you will learnFind out what an effective ML engineering process looks likeUncover options for automating training and deployment and learn how to use themDiscover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutionsUnderstand what aspects of software engineering you can bring to machine learningGain insights into adapting software engineering for machine learning using appropriate cloud technologiesPerform hyperparameter tuning in a relatively automated wayWho this book is for This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If you're someone who manages or wants to understand the production life cycle of these systems, you'll find this book useful. Intermediatelevel knowledge of Python is necessary.
Grokking Deep Learning
By Andrew W. Trask File : Pdf, ePub, Mobi, Kindle
 Publisher : Simon and Schuster
 Book Code : 163835720X
 Total of Pages : 336
 Category : Computers
 Members : 301
 Pdf File: grokkingdeeplearning.pdf
Book Short Summary:
Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, selfdriving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its mathsupporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high schoollevel math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king  man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variablelength data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long shortterm memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
HandsOn QLearning with Python
By Nazia Habib File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1789345758
 Total of Pages : 212
 Category : Computers
 Members : 967
 Pdf File: handsonqlearningwithpython.pdf
Book Short Summary:
Leverage the power of rewardbased training for your deep learning models with Python Key Features Understand Qlearning algorithms to train neural networks using Markov Decision Process (MDP) Study practical deep reinforcement learning using QNetworks Explore statebased unsupervised learning for machine learning models Book Description Qlearning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Qlearning, in addition to helping you get familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree Qlearning and use deep Qnetworks and double deep Qnetworks to solve complex problems. This book will guide you in exploring use cases such as selfdriving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Qnetworks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving realworld problems. You will also explore how to use Qlearning and related algorithms in realworld applications such as scientific research. Toward the end, you’ll gain a sense of what’s in store for reinforcement learning. By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Qlearning algorithms with OpenAI Gym, Keras, and TensorFlow. What you will learn Explore the fundamentals of reinforcement learning and the stateactionreward process Understand Markov decision processes Get well versed with libraries such as Keras, and TensorFlow Create and deploy modelfree learning and deep Qlearning agents with TensorFlow, Keras, and OpenAI Gym Choose and optimize a QNetwork’s learning parameters and finetune its performance Discover realworld applications and use cases of Qlearning Who this book is for If you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decisionmaking in reinforcement learning is assumed.
Applied Software Development With Python & Machine Learning By Wearable & Wireless Systems For Movement Disorder Treatment Via Deep Brain Stimulation
By Robert Lemoyne,Timothy Mastroianni File : Pdf, ePub, Mobi, Kindle
 Publisher : World Scientific
 Book Code : 981123597X
 Total of Pages : 248
 Category : Computers
 Members : 221
 Pdf File: appliedsoftwaredevelopmentwithpythonmachinelearningbywearablewirelesssystemsformovementdisordertreatmentviadeepbrainstimulation.pdf
Book Short Summary:
The book presents the confluence of wearable and wireless inertial sensor systems, such as a smartphone, for deep brain stimulation for treating movement disorders, such as essential tremor, and machine learning. The machine learning distinguishes between distinct deep brain stimulation settings, such as 'On' and 'Off' status. This achievement demonstrates preliminary insight with respect to the concept of Network Centric Therapy, which essentially represents the Internet of Things for healthcare and the biomedical industry, inclusive of wearable and wireless inertial sensor systems, machine learning, and access to Cloud computing resources.Imperative to the realization of these objectives is the organization of the software development process. Requirements and pseudo code are derived, and software automation using Python for postprocessing the inertial sensor signal data to a feature set for machine learning is progressively developed. A perspective of machine learning in terms of a conceptual basis and operational overview is provided. Subsequently, an assortment of machine learning algorithms is evaluated based on quantification of a reach and grasp task for essential tremor using a smartphone as a wearable and wireless accelerometer system.Furthermore, these skills regarding the software development process and machine learning applications with wearable and wireless inertial sensor systems enable new and novel biomedical research only bounded by the reader's creativity.
Practical Deep Learning
By Ron Kneusel File : Pdf, ePub, Mobi, Kindle
 Publisher : No Starch Press
 Book Code : 1718500750
 Total of Pages : 464
 Category : Computers
 Members : 231
 Pdf File: practicaldeeplearning.pdf
Book Short Summary:
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikitlearn and Keras libraries, and evaluate your models’ performance. You’ll also learn: • How to use classic machine learning models like kNearest Neighbors, Random Forests, and Support Vector Machines • How neural networks work and how they’re trained • How to use convolutional neural networks • How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, everexpanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
Python Deep Learning
By Valentino Zocca,Gianmario Spacagna,Daniel Slater,Peter Roelants File : Pdf, ePub, Mobi, Kindle
 Publisher : Packt Publishing Ltd
 Book Code : 1786460661
 Total of Pages : 406
 Category : Computers
 Members : 135
 Pdf File: pythondeeplearning.pdf
Book Short Summary:
Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book Explore and create intelligent systems using cuttingedge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python Get realworld examples and easytofollow tutorials on Theano, TensorFlow, H2O and more Who This Book Is For This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired. What You Will Learn Get a practical deep dive into deep learning algorithms Explore deep learning further with Theano, Caffe, Keras, and TensorFlow Learn about two of the most powerful techniques at the core of many practical deep learning implementations: AutoEncoders and Restricted Boltzmann Machines Dive into Deep Belief Nets and Deep Neural Networks Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Get to know device strategies so you can use deep learning algorithms and libraries in the real world In Detail With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using realworld use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Scikit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside. Style and approach Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects.