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Practical Deep Learning For Coders

Other, , Prof. Jeremy Howard

Updated On 02 Feb, 19

Overview

Learn how to build state of the art models without needing graduate-level mathbut also without dumbing anything down.

This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step onelearning how to get a GPU server online suitable for deep learningand go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems.

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Lecture 5: Lesson 5: Deep Learning 2018

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Lecture Details

NB: Please go to http://course.fast.ai to view this video since there is important updated information there. If you have questions, use the forums at http://forums.fast.ai

You will learn about collaborative filtering through the example of making movie recommendations, and talk about key developments that occurred during the Netflix prize.

We will dig into some lower level details of deep learning: what happens inside the training loop, how optimizers like momentum and Adam work, and regularization using weight decay. You will learn how to think spatially about math concepts like the ‘chain rule’, ‘jacobian’, and ‘hessian’.