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PyTorch Deep Learning Hands-On: Apply modern AI techniques with CNNs, RNNs, GANs, reinforcement learning, and more
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About PyTorch Deep Learning Hands-On: Apply Modern AI
All the key deep learning methods built step-by-step in PyTorch Key Features Understand the internals and principles of PyTorch Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more Build deep learning workflows and take deep learning models from prototyping to production Book Description PyTorch is a new, lightweight, and Python-first tool for deep learning. Built by Facebook to offer flexibility and speed, it has quickly become the preferred tool for deep learning experts. PyTorch helps you release deep learning models faster than ever before. PyTorch Deep Learning Hands-On shows how to implement every major deep learning architecture in PyTorch. Starting with simple neural networks, it covers PyTorch for computer vision (CNN), natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools. Each chapter focuses on a different area of deep learning. Chapters start with a refresher on the core principles, before sharing the code you need to implement them in PyTorch. If you want to become a deep learning expert this book is for you. What you will learn Use PyTorch to build: Simple Neural Networks – build neural networks the PyTorch way, with high-level functions, optimizers, and more Convolutional Neural Networks – create advanced computer vision systems Recurrent Neural Networks – work with sequential data such as natural language and audio Generative Adversarial Networks – create new content with models including SimpleGAN and CycleGAN Reinforcement Learning – develop systems that can solve complex problems such as driving or game playing
