Deep Learning with Python - Learn Best Practices of Deep Learning Models with PyTorch (2nd Edition)

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how w...

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Bibliographic Details
Main Authors: Ketkar, Nikhil, Moolayil, Jojo
Format: eBook Book
Language:English
Published: Berkeley, CA Apress, an imprint of Springer Nature 2021
Apress
Apress L. P
Edition:2
Subjects:
Computer Science
COMPUTERS
Data mining
Machine Learning
Open Source
Professional and Applied Computing
Programming Languages
Python
Python (Computer program language)
Software Engineering
ISBN:9781484253632, 1484253639, 1484253647, 9781484253649
Online Access:Get full text
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Table of Contents:
  • Title Page Introduction Table of Contents 1. Introduction to Machine Learning and Deep Learning 2. Introduction to PyTorch 3. Feed-Forward Neural Networks 4. Automatic Differentiation in Deep Learning 5. Training Deep Leaning Models 6. Convolutional Neural Networks 7. Recurrent Neural Networks 8. Recent Advances in Deep Learning Index
  • Intro -- Table of Contents -- About the Authors -- About the Technical Reviewers -- Acknowledgments -- Introduction -- Chapter 1: Introduction to Machine Learning and Deep Learning -- Defining Deep Learning -- A Brief History -- Rule-Based Systems -- Knowledge-Based Systems -- Machine Learning -- Deep Learning -- Advances in Related Fields -- Prerequisites -- The Approach Ahead -- Installing the Required Libraries -- The Concept of Machine Learning -- Binary Classification -- Regression -- Generalization -- Regularization -- Summary -- Chapter 2: Introduction to PyTorch -- Why Do We Need a Deep Learning Framework? -- What Is PyTorch? -- Why PyTorch? -- It All Starts with a Tensor -- Creating Tensors -- Tensor Munging Operations -- Mathematical Operations -- Element-Wise Mathematical Operations -- Trigonometric Operations in Tensors -- Comparison Operations for Tensors -- Linear Algebraic Operations -- Summary -- Chapter 3: Feed-Forward Neural Networks -- What Is a Neural Network? -- Unit -- The Overall Structure of a Neural Network -- Expressing a Neural Network in Vector Form -- Evaluating the Output of a Neural Network -- Training a Neural Network -- Deriving Cost Functions Using Maximum Likelihood -- Binary Cross-Entropy -- Cross-Entropy -- Squared Error -- Summary of Loss Functions -- Types of Activation Functions -- Linear Unit -- Sigmoid Activation -- Softmax Activation -- Rectified Linear Unit -- Hyperbolic Tangent -- Backpropagation -- Gradient Descent Variants -- Batch Gradient Descent -- Stochastic Gradient Descent -- Mini-Batch Gradient Descent -- Gradient-Based Optimization Techniques -- Gradient Descent with Momentum -- RMSprop -- Adam -- Practical Implementation with PyTorch -- Summary -- Chapter 4: Automatic Differentiation in Deep Learning -- Numerical Differentiation -- Symbolic Differentiation
  • Automatic Differentiation Fundamentals -- Implementing Automatic Differentiation -- What Is Autograd? -- Summary -- Chapter 5: Training Deep Leaning Models -- Performance Metrics -- Classification Metrics -- Regression Metrics -- Mean Squared Error -- Mean Absolute Error -- Mean Absolute Percentage Error -- Data Procurement -- Splitting Data for Training, Validation, and Testing -- Establishing the Achievable Limit on the Error Rate -- Establishing the Baseline with Standard Choices -- Building an Automated, End-to-End Pipeline -- Orchestration for Visibility -- Analysis of Overfitting and Underfitting -- Hyperparameter Tuning -- Model Capacity -- Regularizing the Model -- Early Stopping -- Norm Penalties -- Dropout -- A Practical Implementation in PyTorch -- Interpreting the Business Outcomes for Deep Learning -- Summary -- Chapter 6: Convolutional Neural Networks -- Convolution Operation -- Pooling Operation -- Convolution-Detector-Pooling Building Block -- Stride -- Padding -- Batch Normalization -- Filter -- Filter Depth -- Number of Filters -- Summarizing key learnings from CNNs -- Implementing a basic CNN using PyTorch -- Implementing a larger CNN in PyTorch -- CNN Thumb Rules -- Summary -- Chapter 7: Recurrent Neural Networks -- Introduction to RNNs -- Training RNNs -- Bidirectional RNNs -- Vanishing and Exploding Gradients -- Gradient Clipping -- Long Short-Term Memory -- Practical Implementation -- Summary -- Chapter 8: Recent Advances in Deep Learning -- Going Beyond Classification in Computer Vision -- Object Detection -- Image Segmentation -- Pose Estimation -- Generative Computer Vision -- Natural Language Processing with Deep Learning -- Transformer Models -- Bidirectional Encoder Representations from Transformers -- GrokNet -- Additional Noteworthy Research -- Concluding Thoughts -- Index