Mastering Pytorch Create and Deploy Deep Learning Models from CNNs to Multimodal Models, LLMs, and Beyond
PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classifi...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing Ltd
[2024]
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| Edición: | Second edition |
| Colección: | Expert insight.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009826134906719 |
Tabla de Contenidos:
- Cover
- Copyright
- Contributors
- Table of Contents
- Preface
- Chapter 1: Overview of Deep Learning Using PyTorch
- A refresher on deep learning
- Activation functions
- Optimization schedule
- Exploring the PyTorch library in contrast to TensorFlow
- Tensor modules
- PyTorch modules
- torch.nn
- torch.optim
- torch.utils.data
- Training a neural network using PyTorch
- Summary
- Reference list
- Chapter 2: Deep CNN Architectures
- Why are CNNs so powerful?
- Evolution of CNN architectures
- Developing LeNet from scratch
- Using PyTorch to build LeNet
- Training LeNet
- Testing LeNet
- Fine-tuning the AlexNet model
- Using PyTorch to fine-tune AlexNet
- Running a pretrained VGG model
- Exploring GoogLeNet and Inception v3
- Inception modules
- 1x1 convolutions
- Global average pooling
- Auxiliary classifiers
- Inception v3
- Discussing ResNet and DenseNet architectures
- ResNet
- DenseNet
- Understanding EfficientNets and the future of CNN architectures
- Summary
- References
- Chapter 3: Combining CNNs and LSTMs
- Building a neural network with CNNs and LSTMs
- Text encoding demo
- Building an image caption generator using PyTorch
- Downloading the image captioning datasets
- Preprocessing caption (text) data
- Preprocessing image data
- Defining the image captioning data loader
- Defining the CNN-LSTM model
- Training the CNN-LSTM model
- Generating image captions using the trained model
- Summary
- References
- Chapter 4: Deep Recurrent Model Architectures
- Exploring the evolution of recurrent networks
- Types of recurrent neural networks
- RNNs
- Bidirectional RNNs
- LSTMs
- Extended and bidirectional LSTMs
- Multi-dimensional RNNs
- Stacked LSTMs
- GRUs
- Grid LSTMs
- Gated orthogonal recurrent units
- Training RNNs for sentiment analysis.
- Loading and preprocessing the text dataset
- Instantiating and training the model
- Building a bidirectional LSTM
- Loading and preprocessing the text dataset
- Instantiating and training the LSTM model
- Discussing GRUs and attention-based models
- GRUs and PyTorch
- Attention-based models
- Summary
- References
- Chapter 5: Advanced Hybrid Models
- Building a transformer model for language modeling
- Reviewing language modeling
- Understanding the transformer model architecture
- Defining a transformer model in PyTorch
- Loading and processing the dataset
- Training the transformer model
- Developing a RandWireNN model from scratch
- Understanding RandWireNNs
- Developing RandWireNNs using PyTorch
- Defining a training routine and loading data
- Defining the randomly wired graph
- Defining RandWireNN model modules
- Transforming a random graph into a neural network
- Training the RandWireNN model
- Evaluating and visualizing the RandWireNN model
- Summary
- References
- Chapter 6: Graph Neural Networks
- Introduction to GNNs
- Understanding the intuition behind GNNs
- Using regular NNs on graph data - a thought experiment
- Understanding the power of GNNs with computational graphs
- Types of graph learning tasks
- Understanding node-level tasks
- Understanding edge-level tasks
- Understanding graph-level tasks
- Reviewing prominent GNN models
- Understanding graph convolutions with GCNs
- Using attention in graphs with GAT
- Performing graph sampling with GraphSAGE
- Building a GCN model using PyTorch Geometric
- Loading and exploring the citation networks dataset
- Building a simple NN-based node classifier
- Building a GCN model for node classification
- 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
- Training the transformer-based language model
- Saving and loading the language model
- Using the language model to generate text
- Using GPT models as text generators
- Out-of-the-box text generation with GPT-2
- Text generation strategies using PyTorch
- Greedy search
- Beam search
- Top-k and top-p sampling
- Text generation with GPT-3
- Generating MIDI music with LSTMs using PyTorch
- Loading the MIDI music data
- Defining the LSTM model and training routine
- Training and testing the music generation model
- Summary
- References
- Chapter 8: Neural Style Transfer
- Understanding how to transfer style between images
- Implementing neural style transfer using PyTorch
- Loading the content and style images
- Loading and trimming the pretrained VGG19 model
- Building the neural style transfer model
- Training the style transfer model
- Experimenting with the style transfer system
- Summary
- References
- Chapter 9: Deep Convolutional GANs
- Defining the generator and discriminator networks
- Understanding the DCGAN generator and discriminator
- Training a DCGAN using PyTorch
- Defining the generator
- Defining the discriminator
- Loading the image dataset
- Training loops for DCGANs
- Using GANs for style transfer
- Understanding the pix2pix architecture
- Exploring the pix2pix generator
- Exploring the pix2pix discriminator
- Summary
- References
- Chapter 10: Image Generation Using Diffusion
- Understanding image generation using diffusion
- Understanding how diffusion works
- Training a forward diffusion model
- Performing reverse diffusion or denoising
- Training a diffusion model for image generation
- Loading the dataset using Hugging Face datasets
- Processing the dataset using torchvision transforms.
- Adding noise to images using diffusers
- Defining the UNet model
- Training the UNet model
- Defining the optimizer and learning schedule
- Using Hugging Face Accelerate to accelerate training
- Running the model training loop
- Generating realistic anime images using (reverse) diffusion
- Understanding text-to-image generation using diffusion
- Encoding text input into an embedding vector
- Ingesting additional text data in the (conditional) UNet model
- Using the Stable Diffusion model to generate images from text
- Summary
- Reference list
- Chapter 11: Deep Reinforcement Learning
- Reviewing RL concepts
- Types of RL algorithms
- Model-based
- Model-Free
- Discussing Q-learning
- Understanding deep Q-learning
- Using two separate DNNs
- Experience replay buffer
- Building a DQN model in PyTorch
- Initializing the main and target CNN models
- Defining the experience replay buffer
- Setting up the environment
- Defining the CNN optimization function
- Managing and running episodes
- Training the DQN model to learn Pong
- Summary
- Reference list
- Chapter 12: Model Training Optimizations
- Distributed training with PyTorch
- Training the MNIST model in a regular fashion
- Training the MNIST model in a distributed fashion
- Distributed training on GPUs with CUDA
- Automatic mixed precision training
- Regular model training on a GPU
- Mixed precision training on a GPU
- Summary
- Reference list
- Chapter 13: Operationalizing PyTorch Models into Production
- Model serving in PyTorch
- Creating a PyTorch model inference pipeline
- Saving and loading a trained model
- Building the inference pipeline
- Building a basic model server
- Writing a basic app using Flask
- Using Flask to build our model server
- Setting up model inference for Flask serving
- Building a Flask app to serve model.
- Using a Flask server to run predictions
- Creating a model microservice
- Serving a PyTorch model using TorchServe
- Installing TorchServe
- Launching and using a TorchServe server
- Exporting universal PyTorch models using TorchScript and ONNX
- Understanding the utility of TorchScript
- Model tracing with TorchScript
- Model scripting with TorchScript
- Running a PyTorch model in C++
- Using ONNX to export PyTorch models
- Serving PyTorch models in the cloud
- Using PyTorch with AWS
- Serving a PyTorch model using an AWS instance
- Using TorchServe with Amazon SageMaker
- Serving PyTorch models on Google Cloud
- Serving PyTorch models with Azure
- Working with Azure's DSVMs
- Discussing Azure Machine Learning Service
- Summary
- Reference list
- Chapter 14: PyTorch on Mobile Devices
- Deploying a PyTorch model on Android
- Converting the PyTorch model to a mobile-friendly format
- Setting up the Android app development environment
- Using the phone camera in the Android app to capture images
- Enabling the camera during app startup
- Handling camera permissions in Android
- Opening the camera for image capture
- Capturing images using the phone camera
- Running ML model inference on camera-captured images
- Validating the ML model binary path
- Performing image classification on camera-captured images
- Launching the app on an Android mobile device
- Building PyTorch apps on iOS
- Setting up the iOS development environment
- Using a phone camera in the iOS app to capture images
- Running ML model inference on camera-captured images
- Summary
- Reference list
- Chapter 15: Rapid Prototyping with PyTorch
- Using fastai to set up model training in a few minutes
- Setting up fastai and loading data
- Training an MNIST model using fastai
- Evaluating and interpreting the model using fastai.
- Training models on any hardware using PyTorch Lightning.
