Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow

About

Features

• All students need to get started, and get resultsno machine learning background required.
• Packed with clear, thorough explanations and concise, well-annotated code examples.
• Presents an extensive set of code examples built with TensorFlow and the Keras API, with complementary PyTorch examples provided online.
• Thoroughly demonstrates the use of deep learning in an advanced image captioning network application that combines image and language processing.
• Straight from NVIDIA, creator of the GPU hardware that brings deep learning models to life.

Description

NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results

"To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals."
--From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA

"Ekman uses a learning technique that in our experience has proven pivotal to successasking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us."
--From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute

Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience.

After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images.

Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning.

• Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation
• See how DL frameworks make it easier to develop more complicated and useful neural networks
• Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis
• Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences
• Master NLP with sequence-to-sequence networks and the Transformer architecture
• Build applications for natural language translation and image captioning

NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others.

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Downloads

Downloads

Download the book's code examples:
github.com/NVDLI/LDL

Download large PDF versions of the Appendix J Cheat Sheets:
Cheat Sheets (3.7 MB .zip)

Extras

Author's Site

Please visit the author's site at ldlbook.com, and his company page at nvidia.com/dli-books.

Sample Content

Online Sample Chapter

Deep Learning Frameworks and Network Tweaks

Sample Pages

Download the sample pages (includes Chapter 5

Table of Contents

Preface
Acknowledgments
About the Author

Chapter 1: The Rosenblatt Perceptron
Chapter 2: Gradient-Based Learning
Chapter 3: Sigmoid Neurons and Backpropagation
Chapter 4: Fully Connected Networks Applied to Multiclass Classification
Chapter 5: Toward DL: Frameworks and Network Tweaks
Chapter 6: Fully Connected Networks Applied to Regression
Chapter 7: Convolutional Neural Networks Applied to Image Classification
Chapter 8: Deeper CNNs and Pretrained Models
Chapter 9: Predicting Time Sequences with Recurrent Neural Networks
Chapter 10: Long Short-Term Memory
Chapter 11: Text Autocompletion with LSTM and Beam Search
Chapter 12: Neural Language Models and Word Embeddings
Chapter 13: Word Embeddings from word2vec and GloVe
Chapter 14: Sequence-to-Sequence Networks and Natural Language Translation
Chapter 15: Attention and the Transformer
Chapter 16: One-to-Many Network for Image Captioning
Chapter 17: Medley of Additional Topics
Chapter 18: Summary and Next Steps
Appendix A: Linear Regression and Linear Classifiers
Appendix B: Object Detection and Segmentation
Appendix C: Word embeddings Beyond word2vec and GloVe
Appendix D: GPT, BERT, and RoBERTa
Appendix E: Newton-Raphson versus Gradient Descent
Appendix F: Matrix Implementation of Digit Classification Network
Appendix G: Relating Convolutional Layers to Mathematical Convolution
Appendix H: Gated Recurrent Units
Appendix I: Setting Up a Development Environment
Appendix J: Cheat Sheets

Index

Updates

Errata

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