Build and train neural network models with high speed and flexibility in text, vision, and advanced analytics using PyTorch 1.xKey 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
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
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.
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About the Author
Laura Mitchell graduated with a degree in mathematics from the University of Edinburgh and, since then, has gained over 12 years' experience in the tech and data science space. She is currently lead data scientist at Badoo, which is the largest online dating site in the world with over 400 million users worldwide. Laura has hands-on experience in the delivery of projects such as NLP, image classification, and recommender systems, from initial conception through to production. She has a passion for learning new technologies and keeping up to date with industry trends.
Sri. Yogesh K. is an experienced Data Scientist with a demonstrated history of working in the higher education industry and skilled in Python, Apache Spark, Deep Learning, Hadoop, and Machine Learning. He is a strong engineering professional with a Certificate of Engineering Excellence focused in Big Data Analytics and Optimization from International School of Engineering (INSOFE). Sri has trained 500+ working professionals in Data Science and Deep Learning from companies like Flipkart, Honeywell, GE, Rakuten, etc. Additionally, he has also worked on various projects that involved deep learning and PyTorch.
Vishnu Subramanian has experience in leading, architecting, and implementing several big data analytical projects (artificial intelligence, machine learning, and deep learning). He specializes in machine learning, deep learning, distributed machine learning, and visualization. He has experience in retail, finance, and travel. He is good at understanding and coordinating between businesses, AI, and engineering teams.
Table of ContentsTable 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?