Grokking Deep Learning


Author: Andrew Trask
Publisher: Manning Publications
ISBN: 9781617293702
Category: Computers
Page: 325
View: 6544

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Artificial Intelligence is the most exciting technology of the century, and Deep Learning is, quite literally, the "brain" behind the world's smartest Artificial Intelligence systems out there. Grokking Deep Learning is the perfect place to begin the deep learning journey. Rather than just learning the "black box" API of some library or framework, readers will actually understand how to build these algorithms completely from scratch. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Deep Learning


Author: Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher: MIT Press
ISBN: 0262337371
Category: Computers
Page: 800
View: 1632

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"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.

Deep Learning and the Game of Go


Author: Max Pumperla,Kevin Ferguson
Publisher: Manning Publications
ISBN: 9781617295324
Category: Computers
Page: 325
View: 745

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It's nearly impossible to build a competent Go-playing machine using conventional programming techniques, let alone have it win. By applying advanced AI techniques, in particular deep learning and reinforcement learning, users can train their Go-bot in the rules and tactics of the game. Deep Learning and the Game of Go opens up the world of deep learning and AI by teaching readers to build their own Go-playing machine. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Grokking Algorithms

An Illustrated Guide for Programmers and Other Curious People
Author: Aditya Y. Bhargava
Publisher: Manning Publications
ISBN: 9781617292231
Category: Computers
Page: 256
View: 4031

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Summary Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. Learning about algorithms doesn't have to be boring! Get a sneak peek at the fun, illustrated, and friendly examples you'll find in Grokking Algorithms on Manning Publications' YouTube channel. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology An algorithm is nothing more than a step-by-step procedure for solving a problem. The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to understand them but refuse to slog through dense multipage proofs, this is the book for you. This fully illustrated and engaging guide makes it easy to learn how to use the most important algorithms effectively in your own programs. About the Book Grokking Algorithms is a friendly take on this core computer science topic. In it, you'll learn how to apply common algorithms to the practical programming problems you face every day. You'll start with tasks like sorting and searching. As you build up your skills, you'll tackle more complex problems like data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. By the end of this book, you will have mastered widely applicable algorithms as well as how and when to use them. What's Inside Covers search, sort, and graph algorithms Over 400 pictures with detailed walkthroughs Performance trade-offs between algorithms Python-based code samples About the Reader This easy-to-read, picture-heavy introduction is suitable for self-taught programmers, engineers, or anyone who wants to brush up on algorithms. About the Author Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io. Table of Contents Introduction to algorithms Selection sort Recursion Quicksort Hash tables Breadth-first search Dijkstra's algorithm Greedy algorithms Dynamic programming K-nearest neighbors

The Deep Learning Revolution


Author: Terrence J. Sejnowski
Publisher: MIT Press
ISBN: 026203803X
Category: Computers
Page: 352
View: 4148

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How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

Grokking Bitcoin


Author: Kalle Rosenbaum
Publisher: Manning Publications
ISBN: 9781617294648
Category: Computers
Page: 450
View: 9296

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The modern world turns on universally-accepted ideas of currency and ownership. Bitcoin, and its underlying technology, offer the potential to move control of these key institutions from change-prone governments to a secure storage system that independently records value and ownership in a distributed public ledger called "the blockchain. Grokking Bitcoin opens up this powerful distributed ledger system, exploring the technology that enables applications both for Bitcoin-based financial transactions and using the blockchain for registering physical property ownership. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Deep Learning for Search


Author: Tommaso Teofili
Publisher: N.A
ISBN: 9781617294792
Category: Computers
Page: 325
View: 5557

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Deep Learning for Search teaches readers how to leverage neural networks, NLP, and deep learning techniques to improve search performance. Deep Learning for Search teaches readers how to improve the effectiveness of your search by implementing neural network-based techniques. By the time their finished, they'll be ready to build amazing search engines that deliver the results your users need and get better as time goes on! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Introduction to Deep Learning

From Logical Calculus to Artificial Intelligence
Author: Sandro Skansi
Publisher: Springer
ISBN: 3319730045
Category: Computers
Page: 191
View: 2119

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This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

Deep Learning with R


Author: Francois Chollet,J.j. Allaire
Publisher: Pearson Professional
ISBN: 9781617295546
Category: Computers
Page: 360
View: 7373

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Introduces deep learning systems using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.

Grokking Reactivex


Author: Morgillo,Chignoli,Sasa Sekulic
Publisher: Manning Publications
ISBN: 9781617293498
Category: Computers
Page: 350
View: 5552

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ReactiveX is the common tongue of the reactive programming world. It's a new programming paradigm applied to infinite scenarios using any popular programming language. To really understand Rx, you need to rewire your brain to see the world differently, and we're here to help you. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Grokking ReactiveX is a practical book that teaches readers how to solve complex problems elegantly and with few lines of code. To make learning easier, each chapter introduces a new concept and then immediately demonstrates how to use it. Following carefully-selected examples with thorough, well-paced explanations, readers will immerse themselves in ReactiveX, concept by concept.

Natural Language Processing in Action


Author: Hobson Lane,Cole Howard,Hannes Hapke
Publisher: Pearson Professional
ISBN: 9781617294631
Category: Computers
Page: 420
View: 2396

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Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. In Natural Language Processing in Action, readers explore carefully-chosen examples and expand their machine's knowledge which they can then apply to a range of challenges. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

GANs in Action

Deep Learning with Generative Adversarial Networks
Author: Jakub Langr,Vladimir Bok
Publisher: Manning Publications
ISBN: 9781617295560
Category: Computers
Page: 350
View: 4073

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Deep learning systems have gotten really great at identifying patterns in text, images, and video. But applications that create realistic images, natural sentences and paragraphs, or native-quality translations have proven elusive. Generative Adversarial Networks, or GANs, offer a promising solution to these challenges by pairing two competing neural networks' one that generates content and the other that rejects samples that are of poor quality. GANs in Action: Deep learning with Generative Adversarial Networks teaches you how to build and train your own generative adversarial networks. First, you'll get an introduction to generative modelling and how GANs work, along with an overview of their potential uses. Then, you'll start building your own simple adversarial system, as you explore the foundation of GAN architecture: the generator and discriminator networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

A Deal in Wheat

And Other Stories of the New and Old West
Author: Frank Norris
Publisher: N.A
ISBN: N.A
Category: Western stories
Page: 272
View: 7158

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Algorithms of the Intelligent Web


Author: Douglas G McIlwraith,Haralambos Marmanis,Dmitry Babenko
Publisher: Manning Publications
ISBN: 9781617292583
Category: Computers
Page: 240
View: 5808

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Summary Algorithms of the Intelligent Web, Second Edition teaches the most important approaches to algorithmic web data analysis, enabling you to create your own machine learning applications that crunch, munge, and wrangle data collected from users, web applications, sensors and website logs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Valuable insights are buried in the tracks web users leave as they navigate pages and applications. You can uncover them by using intelligent algorithms like the ones that have earned Facebook, Google, and Twitter a place among the giants of web data pattern extraction. About the Book Algorithms of the Intelligent Web, Second Edition teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs. In this totally revised edition, you'll look at intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Python's scikit-learn. This book guides you through algorithms to capture, store, and structure data streams coming from the web. You'll explore recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning. What's Inside Introduction to machine learning Extracting structure from data Deep learning and neural networks How recommendation engines work About the Reader Knowledge of Python is assumed. About the Authors Douglas McIlwraith is a machine learning expert and data science practitioner in the field of online advertising. Dr. Haralambos Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions. Dmitry Babenko designs applications for banking, insurance, and supply-chain management. Foreword by Yike Guo. Table of Contents Building applications for the intelligent web Extracting structure from data: clustering and transforming your data Recommending relevant content Classification: placing things where they belong Case study: click prediction for online advertising Deep learning and neural networks Making the right choice The future of the intelligent web Appendix - Capturing data on the web

Deep Learning with Python


Author: Francois Chollet
Publisher: Manning Publications
ISBN: 9781617294433
Category: Machine learning
Page: 384
View: 5327

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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 Fran�ois 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 near-unusable speech and image recognition, to near-human 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 Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification 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 Fran�ois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning 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 deep-learning 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

Deep Learning with PyTorch

A practical approach to building neural network models using PyTorch
Author: Vishnu Subramanian
Publisher: Packt Publishing Ltd
ISBN: 1788626079
Category: Computers
Page: 262
View: 2377

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Build neural network models in text, vision and advanced analytics using PyTorch Key Features Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Book Description Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease. What you will learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN’s and generate artistic images using style transfer Who this book is for This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.

Real-World Machine Learning


Author: Henrik Brink,Joseph Richards,Mark Fetherolf
Publisher: N.A
ISBN: 9781617291920
Category: Computers
Page: 400
View: 6525

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In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive amounts of information to offer insights and make decisions in a way that matches or even surpasses human cognitive abilities. These systems use sophisticated computational and statistical tools to build models that can recognize and visualize patterns, predict outcomes, forecast values, and make recommendations. Real-World Machine Learning is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Learn Git in a Month of Lunches


Author: Rick Umali
Publisher: Manning Publications Company
ISBN: 9781617292415
Category: Computers
Page: 376
View: 2133

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Summary Learn Git in a Month of Lunches introduces the discipline of source code control using Git. Whether you're a newbie or a busy pro moving your source control to Git, you'll appreciate how this book concentrates on the components of Git you'll use every day. In easy-to-follow lessons designed to take an hour or less, you'll dig into Git's distributed collaboration model, along with core concepts like committing, branching, and merging. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Git is the source code control system preferred by modern development teams. Its decentralized architecture and lightning-fast branching let you concentrate on your code instead of tedious version control tasks. At first, Git may seem like a sprawling beast. Fortunately, to get started you just need to master a few essential techniques. Read on! Learn Git in a Month of Lunches introduces the discipline of source code control using Git. Helpful for both newbies who have never used source control and busy pros, this book concentrates on the components of Git you'll use every day. In easy-to-follow lessons that take an hour or less, you'll dig into Git's distributed collaboration model, along with core concepts like committing, branching, and merging. This book is a road map to the commands and processes you need to be instantly productive. What's Inside Start from square one—no experience required The most frequently used Git commands Mental models that show how Git works Learn when and how to branch code About the Reader No previous experience with Git or other source control systems is required. About the Author Rick Umali uses Git daily as a developer and is a skilled consultant, trainer, and speaker. Table of Contents Before you begin An overview of Git and version control Getting oriented with Git Making and using a Git repository Using Git with a GUI Tracking and updating files in Git Committing parts of changes The time machine that is Git Taking a fork in the road Merging branches Cloning Collaborating with remotes Pushing your changes Keeping in sync Software archaeology Understanding git rebase Workflows and branching conventions Working with GitHub Third-party tools and Git Sharpening your Git

Machine Learning with TensorFlow


Author: Nishant Shukla
Publisher: Manning Publications
ISBN: 9781617293870
Category: Computers
Page: 272
View: 7601

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Summary Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine. About the Book Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. What's Inside Matching your tasks to the right machine-learning and deep-learning approaches Visualizing algorithms with TensorBoard Understanding and using neural networks About the Reader Written for developers experienced with Python and algebraic concepts like vectors and matrices. About the Author Author Nishant Shukla is a computer vision researcher focused on applying machine-learning techniques in robotics. Senior technical editor, Kenneth Fricklas, is a seasoned developer, author, and machine-learning practitioner. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG A machine-learning odyssey TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS Linear regression and beyond A gentle introduction to classification Automatically clustering data Hidden Markov models PART 3 - THE NEURAL NETWORK PARADIGM A peek into autoencoders Reinforcement learning Convolutional neural networks Recurrent neural networks Sequence-to-sequence models for chatbots Utility landscape

Hands-On Machine Learning with Scikit-Learn and TensorFlow

Concepts, Tools, and Techniques to Build Intelligent Systems
Author: Aurélien Géron
Publisher: "O'Reilly Media, Inc."
ISBN: 1491962267
Category: Computers
Page: 574
View: 7355

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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.