Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách keo gáy, Bìa mềm
Thể loại:Computers - Computer Science
Năm:2019
Ngôn ngữ:english
Trang:304 / 293
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 Description
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.
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 for
This
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.
Table of Contents
Getting Started with Deep Learning Using PyTorch
Building Blocks of Neural Networks
Diving Deep into Neural Networks
Deep Learning for Computer Vision
Natural Language Processing with Sequence data
Implementing Autoencoders
Working with Generative Adversarial Networks
Transfer Learning with Modern Network Architectures
Deep Reinforcement Learning
What Next?
