Book Image

Deep Learning with PyTorch [Video]

By : Anand Saha
Book Image

Deep Learning with PyTorch [Video]

By: Anand Saha

Overview of this book

This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Written in Python, PyTorch is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks. By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You’ll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems. All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Deep-learning-with-PyTorch-video. This course uses Python 3.6, and PyTorch 0.3, while not the latest version available, it provides relevant and informative content for legacy users of Python, and PyTorch.
Table of Contents (6 chapters)
Chapter 4
Sequence Models – RNN for Text Generation
Content Locked
Section 1
Sequence Models Motivation
Get motivated towards the subject. - Know about the practical applications of computer vision in the industry.