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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| Jazyk: | angličtina |
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Berkeley, CA
Apress, an imprint of Springer Nature
2021
Apress Apress L. P |
| Vydání: | 2 |
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| ISBN: | 9781484253632, 1484253639, 1484253647, 9781484253649 |
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| Abstract | 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 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 this book explains the best practices in taking these models to production with PyTorch. |
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| AbstractList | 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 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 this book explains the best practices in taking these models to production with PyTorch. 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 andits building blocksWork with tuning and optimizing models Who This Book Is ForBeginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner. Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated editionwill 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 Learn * Review machine learning fundamentals such as overfitting, underfitting, and regularization. * Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automaticdifferentiation, and stochasticgradient descent. * Apply in-depth linear algebra with PyTorch * Explore PyTorch fundamentals andits building blocks * Work with tuning and optimizing models Who This Book Is For Beginnerswith a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner. 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 ForBeginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner. |
| Author | Ketkar, Nikhil Moolayil, Jojo |
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| Copyright | 2021 Nikhil Ketkar, Jojo Moolayil 2021 |
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| Notes | Includes index |
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| TableOfContents | 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 |
| Title | Deep Learning with Python - Learn Best Practices of Deep Learning Models with PyTorch (2nd Edition) |
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