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Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, NLP, and Transformers using TensorFlow
Cover Page
About This eBook
Halftitle Page
Title Page
Copyright Page
Dedication Page
Contents
Foreword
Foreword
Preface
What Is Deep Learning?
Brief History of Deep Neural Networks
Is This Book for You?
Is DL Dangerous?
Choosing a DL Framework
Prerequisites for Learning DL
About the Code Examples
How to Read This Book
Overview of Each Chapter and Appendix
Acknowledgments
About the Author
Chapter 1. The Rosenblatt Perceptron
Example of a Two-Input Perceptron
The Perceptron Learning Algorithm
Limitations of the Perceptron
Combining Multiple Perceptrons
Implementing Perceptrons with Linear Algebra
Geometric Interpretation of the Perceptron
Understanding the Bias Term
Concluding Remarks on the Perceptron
Chapter 2. Gradient-Based Learning
Intuitive Explanation of the Perceptron Learning Algorithm
Derivatives and Optimization Problems
Solving a Learning Problem with Gradient Descent
Constants and Variables in a Network
Analytic Explanation of the Perceptron Learning Algorithm
Geometric Description of the Perceptron Learning Algorithm
Revisiting Different Types of Perceptron Plots
Using a Perceptron to Identify Patterns
Concluding Remarks on Gradient-Based Learning
Chapter 3. Sigmoid Neurons and Backpropagation
Modified Neurons to Enable Gradient Descent for Multilevel Networks
Which Activation Function Should We Use?
Function Composition and the Chain Rule
Using Backpropagation to Compute the Gradient
Backpropagation with Multiple Neurons per Layer
Programming Example: Learning the XOR Function
Network Architectures
Concluding Remarks on Backpropagation
Chapter 4. Fully Connected Networks Applied to Multiclass Classification
Introduction to Datasets Used When Training Networks
Training and Inference
Extending the Network and Learning Algorithm to Do Multiclass Classification
Network for Digit Classification
Loss Function for Multiclass Classification
Programming Example: Classifying Handwritten Digits
Mini-Batch Gradient Descent
Concluding Remarks on Multiclass Classification
Chapter 5. Toward DL: Frameworks and Network Tweaks
Programming Example: Moving to a DL Framework
The Problem of Saturated Neurons and Vanishing Gradients
Initialization and Normalization Techniques to Avoid Saturated Neurons
Cross-Entropy Loss Function to Mitigate Effect of Saturated Output Neurons
Different Activation Functions to Avoid Vanishing Gradient in Hidden Layers
Variations on Gradient Descent to Improve Learning
Experiment: Tweaking Network and Learning Parameters
Hyperparameter Tuning and Cross-Validation
Concluding Remarks on the Path Toward Deep Learning
Chapter 6. Fully Connected Networks Applied to Regression
Output Units
The Boston Housing Dataset
Programming Example: Predicting House Prices with a DNN
Improving Generalization with Regularization
Experiment: Deeper and Regularized Models for House Price Prediction
Concluding Remarks on Output Units and Regression Problems
Chapter 7. Convolutional Neural Networks Applied to Image Classification
The CIFAR-10 Dataset
Characteristics and Building Blocks for Convolutional Layers
Combining Feature Maps into a Convolutional Layer
Combining Convolutional and Fully Connected Layers into a Network
Effects of Sparse Connections and Weight Sharing
Programming Example: Image Classification with a Convolutional Network
Concluding Remarks on Convolutional Networks
Chapter 8. Deeper CNNs and Pretrained Models
VGGNet
GoogLeNet
ResNet
Programming Example: Use a Pretrained ResNet Implementation
Transfer Learning
Backpropagation for CNN and Pooling
Data Augmentation as a Regularization Technique
Mistakes Made by CNNs
Reducing Parameters with Depthwise Separable Convolutions
Striking the Right Network Design Balance with EfficientNet
Concluding Remarks on Deeper CNNs
Chapter 9. Predicting Time Sequences with Recurrent Neural Networks
Limitations of Feedforward Networks
Recurrent Neural Networks
Mathematical Representation of a Recurrent Layer
Combining Layers into an RNN
Alternative View of RNN and Unrolling in Time
Backpropagation Through Time
Programming Example: Forecasting Book Sales
Dataset Considerations for RNNs
Concluding Remarks on RNNs
Chapter 10. Long Short-Term Memory
Keeping Gradients Healthy
Introduction to LSTM
Alternative View of LSTM
Related Topics: Highway Networks and Skip Connections
Concluding Remarks on LSTM
Chapter 11. Text Autocompletion with LSTM and Beam Search
Encoding Text
Longer-Term Prediction and Autoregressive Models
Beam Search
Programming Example: Using LSTM for Text Autocompletion
Bidirectional RNNs
Different Combinations of Input and Output Sequences
Concluding Remarks on Text Autocompletion with LSTM
Chapter 12. Neural Language Models and Word Embeddings
Introduction to Language Models and Their Use Cases
Examples of Different Language Models
Benefit of Word Embeddings and Insight into How They Work
Word Embeddings Created by Neural Language Models
Programming Example: Neural Language Model and Resulting Embeddings
King – Man + Woman! = Queen
King – Man + Woman ! = Queen
Language Models, Word Embeddings, and Human Biases
Related Topic: Sentiment Analysis of Text
Concluding Remarks on Language Models and Word Embeddings
Chapter 13. Word Embeddings from word2vec and GloVe
Using word2vec to Create Word Embeddings Without a Language Model
Additional Thoughts on word2vec
word2vec in Matrix Form
Wrapping Up word2vec
Programming Example: Exploring Properties of GloVe Embeddings
Concluding Remarks on word2vec and GloVe
Chapter 14. Sequence-to-Sequence Networks and Natural Language Translation
Encoder-Decoder Model for Sequence-to-Sequence Learning
Introduction to the Keras Functional API
Programming Example: Neural Machine Translation
Experimental Results
Properties of the Intermediate Representation
Concluding Remarks on Language Translation
Chapter 15. Attention and the Transformer
Rationale Behind Attention
Attention in Sequence-to-Sequence Networks
Alternatives to Recurrent Networks
Self-Attention
Multi-head Attention
The Transformer
Concluding Remarks on the Transformer
Chapter 16. One-to-Many Network for Image Captioning
Extending the Image Captioning Network with Attention
Programming Example: Attention-Based Image Captioning
Concluding Remarks on Image Captioning
Chapter 17. Medley of Additional Topics
Autoencoders
Multimodal Learning
Multitask Learning
Process for Tuning a Network
Neural Architecture Search
Concluding Remarks
Chapter 18. Summary and Next Steps
Things You Should Know by Now
Ethical AI and Data Ethics
Things You Do Not Yet Know
Next Steps
Appendix A. Linear Regression and Linear Classifiers
Linear Regression as a Machine Learning Algorithm
Computing Linear Regression Coefficients
Classification with Logistic Regression
Classifying XOR with a Linear Classifier
Classification with Support Vector Machines
Evaluation Metrics for a Binary Classifier
Appendix B. Object Detection and Segmentation
Object Detection
Semantic Segmentation
Instance Segmentation with Mask R-CNN
Appendix C. Word Embeddings Beyond word2vec and GloVe
Wordpieces
FastText
Character-Based Method
ELMo
Related Work
Appendix D. GPT, BERT, and RoBERTa
GPT
BERT
RoBERTa
Historical Work Leading Up to GPT and BERT
Other Models Based on the Transformer
Appendix E. Newton-Raphson versus Gradient Descent
Newton-Raphson Root-Finding Method
Relationship Between Newton-Raphson and Gradient Descent
Appendix F. Matrix Implementation of Digit Classification Network
Single Matrix
Mini-Batch Implementation
Appendix G. Relating Convolutional Layers to Mathematical Convolution
Appendix H. Gated Recurrent Units
Alternative GRU Implementation
Network Based on the GRU
Appendix I. Setting Up a Development Environment
Python
Programming Environment
Programming Examples
Datasets
Installing a DL Framework
TensorFlow Specific Considerations
Key Differences Between PyTorch and TensorFlow
Appendix J. Cheat Sheets
Works Cited
Index
Code Snippets
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