Here is a list of 100 chapter titles for a book on PyTorch, focusing on artificial intelligence (AI). These chapters span from basic concepts to advanced AI techniques, helping readers learn to build, train, and deploy AI models using PyTorch.
¶ Part 1: Introduction to PyTorch and AI Fundamentals
- Introduction to PyTorch: A Powerful Framework for AI
- Setting Up Your PyTorch Environment for AI Development
- Understanding Tensors in PyTorch for AI Tasks
- Basic PyTorch Operations: Math and Indexing on Tensors
- Working with PyTorch Autograd for Automatic Differentiation
- Understanding PyTorch's Computational Graph for AI Models
- Introduction to PyTorch Datasets and DataLoader for AI Projects
- How PyTorch Handles GPU Acceleration with CUDA
- Introduction to Neural Networks and Deep Learning Concepts
- Building Your First Neural Network with PyTorch
- Exploring PyTorch's nn.Module for Creating Custom Layers
- PyTorch Optimizers: Stochastic Gradient Descent (SGD) and More
- Tracking Model Performance in PyTorch with Loss Functions
- Introduction to Backpropagation and Gradient Descent in PyTorch
- Debugging and Optimizing PyTorch Models for AI Applications
- Understanding the Basics of Feedforward Neural Networks (FNNs)
- Building a Simple Neural Network for Classification with PyTorch
- Implementing Activation Functions in PyTorch: Sigmoid, ReLU, and Tanh
- Understanding Overfitting and Underfitting in PyTorch Models
- Training Neural Networks with PyTorch: Epochs, Batches, and Learning Rates
- Validation and Testing Neural Networks in PyTorch
- Using Cross-Entropy Loss for Classification Problems in PyTorch
- Introduction to Convolutional Neural Networks (CNNs)
- Building a CNN from Scratch with PyTorch for Image Classification
- Pooling and Padding Layers in PyTorch for CNNs
- Understanding the Vanishing Gradient Problem in Deep Learning
- Exploring Regularization Techniques: Dropout and Batch Normalization in PyTorch
- Saving and Loading Models in PyTorch
- Introduction to Transfer Learning with PyTorch
- Fine-Tuning Pretrained Models for Custom AI Applications with PyTorch
- Exploring Recurrent Neural Networks (RNNs) in PyTorch
- Building an RNN for Sequence Prediction in PyTorch
- Understanding Long Short-Term Memory (LSTM) Networks in PyTorch
- Bidirectional LSTMs and GRUs for Sequence Modeling in PyTorch
- Attention Mechanism in Neural Networks: Transformers in PyTorch
- Understanding the Transformer Architecture for Natural Language Processing (NLP)
- Building a Transformer-based Model in PyTorch
- Autoencoders for Unsupervised Learning with PyTorch
- Generative Adversarial Networks (GANs) in PyTorch
- Training GANs for Image Generation with PyTorch
- Variational Autoencoders (VAEs) and Their Applications in PyTorch
- Implementing Self-Supervised Learning Models in PyTorch
- Exploring Reinforcement Learning Algorithms in PyTorch
- Building Deep Q-Networks (DQN) for Reinforcement Learning with PyTorch
- Multi-Agent Reinforcement Learning with PyTorch
- Introduction to Computer Vision and Image Classification with PyTorch
- Using PyTorch and torchvision for Pretrained Models in Computer Vision
- Image Preprocessing and Augmentation with PyTorch
- Convolutional Neural Networks (CNNs) for Object Detection in PyTorch
- Instance Segmentation with PyTorch: Detecting Objects in Images
- Image Captioning with CNN-RNN Models in PyTorch
- Object Localization and Bounding Boxes with PyTorch
- Building a Style Transfer Model in PyTorch
- Implementing Semantic Segmentation in PyTorch
- Face Recognition with PyTorch and CNNs
- Creating a Facial Landmark Detection System with PyTorch
- Using PyTorch for Optical Character Recognition (OCR)
- Generative Adversarial Networks for Image Synthesis in PyTorch
- Applying Deep Learning to Image Super-Resolution with PyTorch
- Building and Training Image Classification Models with Custom Datasets in PyTorch
- Introduction to Natural Language Processing (NLP) with PyTorch
- Working with Text Data: Tokenization and Vectorization in PyTorch
- Building an NLP Pipeline in PyTorch: Text Preprocessing and Embeddings
- Training a Text Classification Model in PyTorch
- Recurrent Neural Networks (RNNs) for NLP Tasks in PyTorch
- Word Embeddings and Word2Vec Models in PyTorch
- Using GloVe and FastText for Text Representations in PyTorch
- Implementing Named Entity Recognition (NER) with PyTorch
- Building a Question Answering System with PyTorch
- Building an NLP Sentiment Analysis Model with PyTorch
- Text Generation with Recurrent Neural Networks in PyTorch
- Exploring Attention Mechanisms for NLP in PyTorch
- Transformer Networks for Machine Translation in PyTorch
- Pretrained Language Models: BERT and GPT-2 in PyTorch
- Fine-Tuning BERT for Text Classification Tasks in PyTorch
¶ Part 6: Optimizing and Scaling PyTorch Models
- Improving Model Performance: Hyperparameter Tuning with PyTorch
- Learning Rate Scheduling and Optimizer Selection in PyTorch
- Distributed Training in PyTorch with DataParallel and DistributedDataParallel
- GPU Acceleration: Optimizing Training for AI Models in PyTorch
- Using Mixed Precision Training to Speed Up Deep Learning in PyTorch
- Profiling and Debugging PyTorch Models for Efficiency
- Using PyTorch’s JIT Compiler for Optimizing Model Execution
- Optimizing Memory Usage During Training in PyTorch
- Exploring Model Pruning in PyTorch for Smaller Models
- Transfer Learning with PyTorch for Efficient AI Model Training
- Automating Hyperparameter Search in PyTorch
- Handling Large Datasets with PyTorch’s DataLoader
- Model Parallelism: Splitting Models Across Multiple GPUs in PyTorch
- Using PyTorch for Multi-GPU and Multi-node Training
- Leveraging PyTorch for Fast Prototyping and Research
¶ Part 7: AI Deployment and Real-World Applications with PyTorch
- Introduction to Model Deployment with PyTorch
- Exporting PyTorch Models to ONNX for Cross-Platform Deployment
- Serving PyTorch Models with Flask and FastAPI
- Building Scalable AI Services with PyTorch and Docker
- Deploying PyTorch Models with TensorRT for Edge Devices
- Integrating PyTorch Models into Production Pipelines
- Building RESTful APIs for PyTorch Models
- Model Inference on Mobile and Embedded Devices with PyTorch Mobile
- Real-Time Inference and Prediction with PyTorch
- Monitoring and Maintaining PyTorch Models in Production Environments
These chapters guide readers through the entire PyTorch ecosystem, from learning basic operations and neural network implementation to advanced AI tasks such as deep reinforcement learning, computer vision, and NLP. Additionally, the chapters cover essential topics such as model optimization, scaling, and deployment, providing a well-rounded resource for both beginners and advanced users in the AI domain.