Deep Learning with PyTorch 1.x - Second Edition
暫譯: 使用 PyTorch 1.x 的深度學習（第二版）

• Build text classification and language modeling systems using neural networks
• Implement transfer learning using advanced CNN architectures
• Use deep reinforcement learning techniques to solve optimization problems in PyTorch
• Mix multiple models for a powerful ensemble model
• Build image classifiers by implementing CNN architectures using PyTorch
• Get up to speed with reinforcement learning, GANs, LSTMs, and RNNs with real-world examples

PyTorch is gaining the attention of deep learning researchers and data science professionals due to its accessibility and efficiency, along with the fact that it's more native to the Python way of development. This book will get you up and running with this cutting-edge deep learning library, effectively guiding you through implementing deep learning concepts.

In this second edition, you'll learn the fundamental aspects that power modern deep learning, and explore the new features of the PyTorch 1.x library. You'll understand how to solve real-world problems using CNNs, RNNs, and LSTMs, along with discovering state-of-the-art modern deep learning architectures, such as ResNet, DenseNet, and Inception. You'll then focus on applying neural networks to domains such as computer vision and NLP. Later chapters will demonstrate how to build, train, and scale a model with PyTorch and also cover complex neural networks such as GANs and autoencoders for producing text and images. In addition to this, you'll explore GPU computing and how it can be used to perform heavy computations. Finally, you'll learn how to work with deep learning-based architectures for transfer learning and reinforcement learning problems.

By the end of this book, you'll be able to confidently and easily implement deep learning applications in PyTorch.

• Gain a thorough understanding of the PyTorch framework and learn to implement neural network architectures
• Understand GPU computing to perform heavy deep learning computations using Python
• Apply cutting-edge natural language processing (NLP) techniques to solve problems with textual data