Practical Deep Learning with PyTorch

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by DataRoot Labs

A Product company founded in 2016 with offices in Kyiv, Ukraine.

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In the 16-week course, you will get an in-depth understanding of how to create, train, test, and deploy a Neural Network using one of the most popular Deep Learning frameworks — PyTorch. You will get a comprehensive theoretical knowledge of everything you need to know from a simple Feedforward Neural Network to state-of-the-art NLP and computer vision models. Besides, you will gain a lot of practical experience through completing our labs, where you will build Neural Networks to detect tumor regions in the brain, generate CryptoPunks, and even create music in the style of Chopin (and many more!) Additionally, you will have a chance to test your practical skills by working with real datasets in Kaggle projects. After completion, you'll be able to apply for the Deep Learning Researcher position on the dHired platform. A list of recommended jobs is present below. Remember, only the best ones will get a job!

Target audience

This course is recommended for those who have a solid understanding of Python and are familiar with the basics of Machine Learning. We highly encourage you to check out our Data Science Fundamentals course to get the prerequisites needed for this course.

Ivan Didur
Ivan Didur
Joshua Reuben
Joshua Reuben
System Architect @ Cognyte

Course syllabus
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Introduction to Deep Learning and Neural Networks

In this module, we will focus on the theoretical concepts behind Deep Learning. You will learn what a Neural Network is mathematically and how to implement it in practice. We will cover everything from linear algebra in the Neural Network to hyperparameter tuning and regularization techniques. In the end, you will have a chance to test your knowledge on a test.

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Introduction to Pytorch

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Convolutional Neural Networks

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Recurrent Neural Networks

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Generative Models

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Attention & Transformers

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Deploying PyTorch models

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