PyTorch Machine Learning: Practical Guide to Building, Training & Deploying Deep Learning Models with Python

Master PyTorch and Build Production-Ready Deep Learning Models from Scratch to Deployment

•Complete PyTorch curriculum covering tensors, neural networks, CNNs, RNNs, Transformers, GANs, and reinforcement learning
•Production deployment skills including model optimization, quantization, ONNX export, TorchScript, and serving with Flask & Docker
•Real-world projects featuring image segmentation (U-Net), object detection (YOLO), NLP with Transformers, and GANs
•Advanced techniques including mixed precision training, distributed training, hyperparameter tuning with Optuna, and explainable AI

Book Description

Transform your Python skills into deep learning expertise with this comprehensive guide to PyTorch. Whether you're building your first neural network or deploying production AI systems, this book provides everything you need to succeed.

Starting with PyTorch fundamentals—tensors, autograd, and neural network basics—you'll quickly progress to building sophisticated models using the nn.Module class, custom layers, and advanced architectures. Master data handling with PyTorch's dataset and dataloader classes, implement data augmentation, and leverage transfer learning with pre-trained models.

Dive deep into convolutional neural networks for computer vision tasks and recurrent networks (LSTMs, GRUs) with attention mechanisms for sequence processing. Learn to implement complete training loops with proper loss computation, backpropagation, optimization algorithms, learning rate schedules, and regularization techniques.

Take your skills further with advanced topics including custom loss functions and optimizers, mixed precision training, distributed training across multiple GPUs, and automated hyperparameter tuning using grid search, Bayesian optimization, and Optuna. Master model evaluation with cross-validation, confusion matrices, ROC curves, and gradient-based interpretation methods.

The book emphasizes production-ready skills with dedicated coverage of model deployment. Learn quantization and pruning for model optimization, export models using ONNX and TorchScript, and serve them in production environments using Flask and Docker.

Apply your knowledge through hands-on projects: build image segmentation systems with U-Net, implement object detection with YOLO, create NLP applications using Transformers, develop generative models with GANs, and explore reinforcement learning applications.

What You Will Learn

•Build neural networks from scratch using PyTorch tensors, autograd, and nn.Module
•Implement CNNs for image classification and RNNs/LSTMs/GRUs for sequence tasks
•Master data preprocessing, augmentation, and transfer learning techniques
•Create custom layers, loss functions, and optimization algorithms
•Train models efficiently with mixed precision and distributed training
•Optimize hyperparameters using Optuna, grid search, and Bayesian methods
•Deploy production models with quantization, ONNX, TorchScript, Flask, and Docker
•Build advanced projects: U-Net segmentation, YOLO detection, Transformers, GANs, and RL
•Leverage TorchVision, TorchText, TorchAudio, and PyTorch Lightning
•Debug, profile, and optimize PyTorch code for maximum performance

Who This Book Is For

Python programmers transitioning to deep learning, data scientists adding neural networks to their toolkit, machine learning practitioners learning PyTorch, computer science students seeking practical AI skills, and software engineers building production ML systems.