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Title Machine Learning Pocket Reference
Author Matt Harrison
Publisher O'Reilly Media
Release Date 2019-08-27
Category Computers
Total Pages 320
ISBN 9781492047513
Language English, Spanish, and French
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Book Summary:

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines

Title Machine Learning Pocket Reference
Author Matt Harrison
Publisher "O'Reilly Media, Inc."
Release Date 2019-08-27
Category Computers
Total Pages 320
ISBN 9781492047490
Language English, Spanish, and French
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Book Summary:

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines

Title Machine Learning Pocket Reference
Author Matthew Harrison
Publisher Unknown
Release Date 2019
Category Machine learning
Total Pages 200
ISBN 1492047538
Language English, Spanish, and French
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Book Summary:

With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines.

Title Machine Learning Quick Reference
Author Rahul Kumar
Publisher Packt Publishing Ltd
Release Date 2019-01-31
Category Computers
Total Pages 294
ISBN 9781788831611
Language English, Spanish, and French
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Book Summary:

Your hands-on reference guide to developing, training, and optimizing your machine learning models Key Features Your guide to learning efficient machine learning processes from scratch Explore expert techniques and hacks for a variety of machine learning concepts Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems Book Description Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference. What you will learn Get a quick rundown of model selection, statistical modeling, and cross-validation Choose the best machine learning algorithm to solve your problem Explore kernel learning, neural networks, and time-series analysis Train deep learning models and optimize them for maximum performance Briefly cover Bayesian techniques and sentiment analysis in your NLP solution Implement probabilistic graphical models and causal inferences Measure and optimize the performance of your machine learning models Who this book is for If you’re a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you’re an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You’ll need some exposure to machine learning to get the best out of this book.

Generative Deep Learning by David Foster

Title Generative Deep Learning
Author David Foster
Publisher "O'Reilly Media, Inc."
Release Date 2019-06-28
Category Computers
Total Pages 330
ISBN 9781492041894
Language English, Spanish, and French
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Book Summary:

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Title Python Pocket Reference
Author Mark Lutz
Publisher "O'Reilly Media, Inc."
Release Date 2014-01-22
Category Computers
Total Pages 266
ISBN 9781449356941
Language English, Spanish, and French
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Book Summary:

Updated for both Python 3.4 and 2.7, this convenient pocket guide is the perfect on-the-job quick reference. You’ll find concise, need-to-know information on Python types and statements, special method names, built-in functions and exceptions, commonly used standard library modules, and other prominent Python tools. The handy index lets you pinpoint exactly what you need. Written by Mark Lutz—widely recognized as the world’s leading Python trainer—Python Pocket Reference is an ideal companion to O’Reilly’s classic Python tutorials, Learning Python and Programming Python, also written by Mark. This fifth edition covers: Built-in object types, including numbers, lists, dictionaries, and more Statements and syntax for creating and processing objects Functions and modules for structuring and reusing code Python’s object-oriented programming tools Built-in functions, exceptions, and attributes Special operator overloading methods Widely used standard library modules and extensions Command-line options and development tools Python idioms and hints The Python SQL Database API

Tensorflow For Deep Learning by Bharath Ramsundar

Title TensorFlow for Deep Learning
Author Bharath Ramsundar
Publisher "O'Reilly Media, Inc."
Release Date 2018-03-01
Category Computers
Total Pages 256
ISBN 9781491980408
Language English, Spanish, and French
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Book Summary:

Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It’s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units

Title Thoughtful Machine Learning with Python
Author Matthew Kirk
Publisher "O'Reilly Media, Inc."
Release Date 2017-01-16
Category Computers
Total Pages 216
ISBN 9781491924105
Language English, Spanish, and French
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Book Summary:

Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms

Tensor Flow Pocket Primer by Oswald Campesato

Title Tensor Flow Pocket Primer
Author Oswald Campesato
Publisher Stylus Publishing, LLC
Release Date 2019-05-09
Category Computers
Total Pages 200
ISBN 9781683923657
Language English, Spanish, and French
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Book Summary:

This book introduces TensorFlow to people who have some knowledge of Python. Readers will learn about many “core” TensorFlow APIs, Linear Regression, Logistic Regression, and MultiLayer Perceptrons. This book is meant to provide both a foundation in TensorFlow and a rudimentary understanding of Deep Learning. Features: - A concrete introduction to TensorFlow and useful APIs - Companion files with code from the text. eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at [email protected]

Title Regular Expression Pocket Reference
Author Tony Stubblebine
Publisher "O'Reilly Media, Inc."
Release Date 2003-08-27
Category Computers
Total Pages 112
ISBN 1449378862
Language English, Spanish, and French
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Book Summary:

Regular expressions are such a powerful tool for manipulating text and data that anyone who uses a computer can benefit from them. Composed of a mixture of symbols and text, regular expressions can be an outlet for creativity, for brilliant programming, and for the elegant solution. While a command of regular expressions is an invaluable skill, all there is to know about them fills a very large volume, and you don't always have time to thumb through hundreds of pages each time a question arises. The answer is the Regular Expression Pocket Reference. Concise and easy-to-use, this little book is the portable companion to Mastering Regular Expressions. This handy guide offers programmers a complete overview of the syntax and semantics of regular expressions that are at the heart of every text-processing application. Ideal as an introduction for beginners and a quick reference for advanced programmers, Regular Expression Pocket Reference is a comprehensive guide to regular expression APIs for C, Perl, PHP,Java, .NET, Python, vi, and the POSIX regular expression libraries. O'Reilly's Pocket References have become a favorite among programmers everywhere. By providing a wealth of important details in a concise, well-organized format, these handy books deliver just what you need to complete the task at hand. When you've reached a sticking point and need to get to a solution quickly, the new Regular Expression Pocket Reference is the book you'll want to have.

Bash Pocket Reference by Arnold Robbins

Title Bash Pocket Reference
Author Arnold Robbins
Publisher "O'Reilly Media, Inc."
Release Date 2016-02-17
Category Computers
Total Pages 156
ISBN 9781491941546
Language English, Spanish, and French
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Book Summary:

It’s simple: if you want to interact deeply with Mac OS X, Linux, and other Unix-like systems, you need to know how to work with the Bash shell. This concise little book puts all of the essential information about Bash right at your fingertips. You’ll quickly find answers to the annoying questions that generally come up when you’re writing shell scripts: What characters do you need to quote? How do you get variable substitution to do exactly what you want? How do you use arrays? Updated for Bash version 4.4, this book has the answers to these and other problems in a format that makes browsing quick and easy. Topics include: Invoking the shell Syntax Functions and variables Arithmetic expressions Command history Programmable completion Job control Shell options Command execution Coprocesses Restricted shells Built-in commands

Title Hands On Unsupervised Learning Using Python
Author Ankur A. Patel
Publisher "O'Reilly Media, Inc."
Release Date 2019-02-21
Category Computers
Total Pages 362
ISBN 9781492035596
Language English, Spanish, and French
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Book Summary:

Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks

Tensorflow 2 Pocket Primer by Oswald Campesato

Title TensorFlow 2 Pocket Primer
Author Oswald Campesato
Publisher Stylus Publishing, LLC
Release Date 2019-08-27
Category Computers
Total Pages 252
ISBN 9781683924593
Language English, Spanish, and French
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Book Summary:

As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to basic machine learning algorithms using TensorFlow 2. It is intended to be a fast-paced introduction to various “core” features of TensorFlow, with code samples that cover machine learning and TensorFlow basics. A comprehensive appendix contains some Keras-based code samples and the underpinnings of MLPs, CNNs, RNNs, and LSTMs. The material in the chapters illustrates how to solve a variety of tasks after which you can do further reading to deepen your knowledge. Companion files with all of the code samples are available for downloading from the publisher by emailing proof of purchase to [email protected] Features: Uses Python for code samples Covers TensorFlow 2 APIs and Datasets Includes a comprehensive appendix that covers Keras and advanced topics such as NLPs, MLPs, RNNs, LSTMs Features the companion files with all of the source code examples and figures (download from the publisher)

Deep Learning by Ian Goodfellow

Title Deep Learning
Author Ian Goodfellow
Publisher MIT Press
Release Date 2016-11-10
Category Computers
Total Pages 800
ISBN 9780262337373
Language English, Spanish, and French
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Book Summary:

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Title Building Machine Learning Powered Applications
Author Emmanuel Ameisen
Publisher "O'Reilly Media, Inc."
Release Date 2020-01-21
Category Computers
Total Pages 260
ISBN 9781492045069
Language English, Spanish, and French
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Book Summary:

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Define your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy and monitor your models in a production environment

Title Practical AI on the Google Cloud Platform
Author Micheal Lanham
Publisher "O'Reilly Media, Inc."
Release Date 2020-10-20
Category Computers
Total Pages 394
ISBN 9781492075769
Language English, Spanish, and French
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Book Summary:

Working with AI is complicated and expensive for many developers. That's why cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools to get you started. With this book, you'll learn how to use Google's AI-powered cloud services to do everything from creating a chatbot to analyzing text, images, and video. Author Micheal Lanham demonstrates methods for building and training models step-by-step and shows you how to expand your models to accomplish increasingly complex tasks. If you have a good grasp of math and the Python language, you'll quickly get up to speed with Google Cloud Platform, whether you want to build an AI assistant or a simple business AI application. Learn key concepts for data science, machine learning, and deep learning Explore tools like Video AI and AutoML Tables Build a simple language processor using deep learning systems Perform image recognition using CNNs, transfer learning, and GANs Use Google's Dialogflow to create chatbots and conversational AI Analyze video with automatic video indexing, face detection, and TensorFlow Hub Build a complete working AI agent application

Machine Learning With Aws by Jeffrey Jackovich

Title Machine Learning with AWS
Author Jeffrey Jackovich
Publisher Packt Publishing Ltd
Release Date 2018-11-06
Category Computers
Total Pages 254
ISBN 9781789809572
Language English, Spanish, and French
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Book Summary:

Use artificial intelligence and machine learning on AWS to create engaging applications Key Features Explore popular AI and ML services with their underlying algorithms Use the AWS environment to manage your AI workflow Reinforce key concepts with hands-on exercises using real-world datasets Book Description Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable. From natural language processing (NLP) applications, such as language translation and understanding news articles and other text sources, to creating chatbots with both voice and text interfaces, you will learn all that there is to know about using AWS to your advantage. You will also understand how to process huge numbers of images fast and create machine learning models. By the end of this book, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects. What you will learn Get up and running with machine learning on the AWS platform Analyze unstructured text using AI and Amazon Comprehend Create a chatbot and interact with it using speech and text input Retrieve external data via your chatbot Develop a natural language interface Apply AI to images and videos with Amazon Rekognition Who this book is for Machine Learning with AWS is ideal for data scientists, programmers, and machine learning enthusiasts who want to learn about the artificial intelligence and machine learning capabilities of Amazon Web Services.

Title Machine Learning Applications Using Python
Author Puneet Mathur
Publisher Apress
Release Date 2019-02-08
Category Computers
Total Pages 379
ISBN 9781484237878
Language English, Spanish, and French
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Book Summary:

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will Learn Discover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals.

Title Hands On Machine Learning with Scikit Learn and TensorFlow
Author Aurélien Géron
Publisher "O'Reilly Media, Inc."
Release Date 2017-03-13
Category Computers
Total Pages 574
ISBN 9781491962268
Language English, Spanish, and French
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Book 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.

Title Natural Language Processing with PyTorch
Author Delip Rao
Publisher O'Reilly Media
Release Date 2019-01-22
Category Computers
Total Pages 256
ISBN 9781491978207
Language English, Spanish, and French
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Book Summary:

Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems