Build and train neural network models with high speed and flexibility in text, vision, and advanced analytics using PyTorch 1.x
Key Features
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
Book DescriptionPyTorch 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.
What you will learn
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
Who this book is forThis book is for data scientists and machine learning engineers looking to work with deep learning algorithms using PyTorch 1.x. You will also find this book useful if you want to migrate to PyTorch 1.x. Working knowledge of Python programming and some understanding of machine learning will be helpful.
Key Features
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
Book DescriptionPyTorch 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.
What you will learn
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
Who this book is forThis book is for data scientists and machine learning engineers looking to work with deep learning algorithms using PyTorch 1.x. You will also find this book useful if you want to migrate to PyTorch 1.x. Working knowledge of Python programming and some understanding of machine learning will be helpful.
