Deep Learning with PyTorch (Video): Start your deep learning journey with PyTorch

Anand Saha

$137.99

Video Apr 2018 4hrs 42mins 1st Edition

Anand Saha

$137.99

Video Apr 2018 4hrs 42mins 1st Edition

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Key benefits

Explore PyTorch and the impact it has made on Deep Learning
Design and implement powerful neural networks to solve some impressive problems in a step-by-step manner
Follow the examples to solve similar use cases outside this course

Description

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.

Who is this book for?

This course is for Python programmers who have some knowledge of machine learning and want to build Deep Learning systems with PyTorch. Python programming knowledge and minimal math skills (matrix/vector manipulation, simple probabilities) is assumed.

What you will learn

Understand PyTorch and Deep Learning concepts
Build your neural network using Deep Learning techniques in PyTorch.
Perform basic operations on your dataset using tensors and variables
Build artificial neural networks in Python with GPU acceleration
See how CNN works in PyTorch with a simple computer vision example
Train your RNN model from scratch for text generation
Use Auto Encoders in PyTorch to remove noise from images
Perform reinforcement learning to solve OpenAI's Cartpole task
Extend your knowledge of Deep Learning by using PyTorch to solve your own machine learning problems

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