Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch

Description

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 with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms.  You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. What You'll LearnReview machine learning fundamentals such as overfitting, underfitting, and regularization.Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.Apply in-depth linear algebra with PyTorchExplore PyTorch fundamentals and its building blocksWork with tuning and optimizing models Who This Book Is For Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.      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