PyTorch Deep Learning in 7 Days [Video]

PyTorch Deep Learning in 7 Days [Video]

Will Ballard
New Release!

Seven short lessons and a daily exercise, carefully chosen to get you started with PyTorch Deep Learning faster than other courses
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Video Details

ISBN 13 9781789135367
Course Length 2 hours 9 minutes

Table of Contents

Video Description

PyTorch is Facebook’s latest Python-based framework for Deep Learning. It has the ability to create dynamic Neural Networks on CPUs and GPUs, both with a significantly less code compared to other competing frameworks. PyTorch has a unique interface that makes it as easy to learn as NumPy.

This 7-day course is for those who are in a hurry to get started with PyTorch. You will be introduced to the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. This course is an attempt to break the myth that Deep Learning is complicated and show you that with the right choice of tools combined with a simple and intuitive explanation of core concepts, Deep Learning is as accessible as any other application development technologies out there. It’s a journey from diving deep into the fundamentals to getting acquainted with the advance concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks.

By the end of the course, you will be able to build Deep Learning applications with PyTorch.

All the code and supporting files for this course are available on GitHub at: https://github.com/PacktPublishing/PyTorch-Deep-Learning-in-7-Days

Style and Approach

This hands-on course will get you up-and-running with PyTorch in a week. It is composed of seven lessons. Each video covers one single concept or a set of code modules explained via step-by-step code walkthrough. The complete lesson is systematically explained and is followed by an assignment to spend time on your own as an exercise.

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What You Will Learn

  • Get comfortable with most commonly used PyTorch concepts, modules and API including Tensor operations, data representations, and manipulation
  • Work with Deep Learning models and architectures including layers, activations, loss functions, gradients, chain rule, forward and backward passes, and optimizers
  • Apply Deep Learning architectures to solve Machine Learning problems for Structured Datasets, Computer Vision, and Natural Language Processing
  • Utilize the concept of Transfer Learning by using pre-trained Deep Learning models to your own problems
  • Implement state of the art in Natural Language Processing to solve real-world problems such as sentiment analysis
  • Implement a simple Generative Adversarial Network to generate fancy images after training on a large image dataset

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Table of Contents

Video Details

ISBN 139781789135367
Course Length2 hours 9 minutes
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