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Preface
Chapter 1: Overview of Deep Learning Using PyTorch
A refresher on deep learning
Optimization schedule
Exploring the PyTorch library in contrast to TensorFlow
Summary
Reference list
Chapter 2: Deep CNN Architectures
Why are CNNs so powerful?
Evolution of CNN architectures
Developing LeNet from scratch
Fine-tuning the AlexNet model
Running a pretrained VGG model
Exploring GoogLeNet and Inception v3
Discussing ResNet and DenseNet architectures
Understanding EfficientNets and the future of CNN architectures
Summary
References
Chapter 3: Combining CNNs and LSTMs
Building a neural network with CNNs and LSTMs
Building an image caption generator using PyTorch
Summary
References
Chapter 4: Deep Recurrent Model Architectures
Exploring the evolution of recurrent networks
Training RNNs for sentiment analysis
Building a bidirectional LSTM
Discussing GRUs and attention-based models
Summary
References
Chapter 5: Advanced Hybrid Models
Building a transformer model for language modeling
Developing a RandWireNN model from scratch
Summary
References
Chapter 6: Graph Neural Networks
Introduction to GNNs
Types of graph learning tasks
Reviewing prominent GNN models
Training a GAT model with PyTorch Geometric
Summary
Reference list
Chapter 7: Music and Text Generation with PyTorch
Building a transformer-based text generator with PyTorch
Using GPT models as text generators
Generating MIDI music with LSTMs using PyTorch
Summary
References
Chapter 8: Neural Style Transfer
Understanding how to transfer style between images
Implementing neural style transfer using PyTorch
Summary
References
Chapter 9: Deep Convolutional GANs
Defining the generator and discriminator networks
Training a DCGAN using PyTorch
Using GANs for style transfer
Summary
References
Chapter 10: Image Generation Using Diffusion
Understanding image generation using diffusion
Training a diffusion model for image generation
Understanding text-to-image generation using diffusion
Using the Stable Diffusion model to generate images from text
Summary
Reference list
Chapter 11: Deep Reinforcement Learning
Reviewing RL concepts
Discussing Q-learning
Understanding deep Q-learning
Building a DQN model in PyTorch
Summary
Reference list
Chapter 12: Model Training Optimizations
Distributed training with PyTorch
Distributed training on GPUs with CUDA
Summary
Reference list
Chapter 13: Operationalizing PyTorch Models into Production
Model serving in PyTorch
Building a basic model server
Creating a model microservice
Serving a PyTorch model using TorchServe
Exporting universal PyTorch models using TorchScript and ONNX
Running a PyTorch model in C++
Using ONNX to export PyTorch models
Serving PyTorch models in the cloud
Summary
Chapter 14: PyTorch on Mobile Devices
Deploying a PyTorch model on Android
Using the phone camera in the Android app to capture images
Building PyTorch apps on iOS
Summary
Reference list
Chapter 15: Rapid Prototyping with PyTorch
Using fastai to set up model training in a few minutes
Training models on any hardware using PyTorch Lightning
Profiling MNIST model inference using PyTorch Profiler
Summary
Reference list
Chapter 16: PyTorch and AutoML
Finding the best neural architectures with AutoML
Using Optuna for hyperparameter search
Summary
Reference list
Chapter 17: PyTorch and Explainable AI
Model interpretability in PyTorch
Using Captum to interpret models
Summary
Reference List
Chapter 18: Recommendation Systems with PyTorch
Using deep learning for recommendation systems
Understanding and processing the MovieLens dataset
Training and evaluating a recommendation system model
Building a recommendation system using the trained model
Summary
Reference list
Chapter 19: PyTorch and Hugging Face
Understanding Hugging Face within the PyTorch context
Using the Hugging Face Hub for pre-trained models
Using the Hugging Face Datasets library with PyTorch
Using Accelerate to speed up PyTorch model training
Using Optimum to optimize PyTorch model deployment
Summary
Reference list
Index |
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