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Mastering PyTorch
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ISBN 9781801074308
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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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