Intro to Deep Learning with PyTorch

Online Course

Udacity

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Prerequisites & Facts
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What is the course about?

Intro to Deep Learning with PyTorch

The course Intro to Deep Learning with PyTorch is an online class provided by Udacity. The skill level of the course is Intermediate. It may be possible to receive a verified certification or use the course to prepare for a degree.

In this course, you’ll learn the basics of deep learning, and build your own deep neural networks using PyTorch. You’ll get practical experience with PyTorch through coding exercises and projects implementing state-of-the-art AI applications such as style transfer and text generation.

Course description

Intro to Deep Learning with PyTorch
2 months

Use PyTorch to implement your first deep neural network
Build Deep Learning Models Today

Introduction to Deep Learning
Introduction to PyTorch
Deep Learning with PyTorch
Convolutional Neural Networks
Style Transfer
Recurrent Neural Networks
Natural Language Classification
Deploying with PyTorch

Discover the basic concepts of deep learning such as neural networks and gradient descent
Implement a neural network in NumPy and train it using gradient descent with in-class programming exercises
Build a neural network to predict student admissions
Hear from Soumith Chintala, the creator of PyTorch, how the framework came to be, where it’s being used now, and how it’s changing the future of deep learning
Build your first neural network with PyTorch to classify images of clothing
Work through a set of Jupyter Notebooks to learn the major components of PyTorch
Load a pre-trained neural network to build a state-of-the-art image classifier
Use PyTorch to build Convolutional Neural Networks for state-of-the-art computer vision applications
Train a convolutional network to classify dog breeds from images of dogs
Use a pre-trained convolutional network to create new art by merging the style of one image with the content of another image
Implement the paper “A Neural Algorithm of Artistic Style” by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge”
Build recurrent neural networks with PyTorch that can learn from sequential data such as natural language
Implement a network that learns from Tolstoy’s Anna Karenina to generate new text based on the novel
Use PyTorch to implement a recurrent neural network that can classify text
Use your network to predict the sentiment of movie reviews
Soumith Chintala teaches you how to deploy deep learning models with PyTorch
Build a chatbot and compile the network for deployment in a production environment

To succeed in this course, you’ll need to be comfortable with Python and data processing libraries such as NumPy and Matplotlib. Basic knowledge of linear algebra and calculus is recommended, but isn’t required to complete the exercises.
See the Technology Requirements for using Udacity.

Deep learning is driving the AI revolution and PyTorch is making it easier than ever for anyone to build deep learning applications. In this course, you’ll gain practical experience building and training deep neural networks using PyTorch. You’ll be able to use these skills on your own personal projects.

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Prerequisites & Facts

Intro to Deep Learning with PyTorch

Course Topic

Deep Learning

University, College, Institution

Udacity

Course Skill Level

Intermediate

see course summary

Course Language

English

Place of class

Online, self-paced (see curriculum for more information)

Degree

Certificate

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Degree & Cost

Intro to Deep Learning with PyTorch

To obtain a verified certificate from Udacity you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.

Udacity

Intro to Deep Learning with PyTorch

provided by Udacity

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