更新时间：2021-06-24 19:17:02
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Foreword
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Preface
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Getting Started with Deep Learning Using PyTorch
Artificial intelligence
The history of AI
Machine learning
Examples of machine learning in real life
Deep learning
Applications of deep learning
Hype associated with deep learning
The history of deep learning
Why now?
Hardware availability
Data and algorithms
Deep learning frameworks
PyTorch
Summary
Building Blocks of Neural Networks
Installing PyTorch
Our first neural network
Data preparation
Scalar (0-D tensors)
Vectors (1-D tensors)
Matrix (2-D tensors)
3-D tensors
Slicing tensors
4-D tensors
5-D tensors
Tensors on GPU
Variables
Creating data for our neural network
Creating learnable parameters
Neural network model
Network implementation
Loss function
Optimize the neural network
Loading data
Dataset class
DataLoader class
Diving Deep into Neural Networks
Deep dive into the building blocks of neural networks
Layers – fundamental blocks of neural networks
Non-linear activations
Sigmoid
Tanh
ReLU
Leaky ReLU
PyTorch non-linear activations
The PyTorch way of building deep learning algorithms
Model architecture for different machine learning problems
Loss functions
Optimizing network architecture
Image classification using deep learning
Loading data into PyTorch tensors
Loading PyTorch tensors as batches
Building the network architecture
Training the model
Fundamentals of Machine Learning
Three kinds of machine learning problems
Supervised learning
Unsupervised learning
Reinforcement learning
Machine learning glossary
Evaluating machine learning models
Training validation and test split
Simple holdout validation
K-fold validation
K-fold validation with shuffling
Data representativeness
Time sensitivity
Data redundancy
Data preprocessing and feature engineering
Vectorization
Value normalization
Handling missing values
Feature engineering
Overfitting and underfitting
Getting more data
Reducing the size of the network
Applying weight regularization
