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Cutting Edge Deep Learning for Coders

Other,, Summer 2018 , Prof. Jeremy Howard

Updated On 02 Feb, 19

Overview

Welcome to thenew 2018 editionof fast.ai's second 7 week course,Cutting Edge Deep Learning For Coders, Part 2, where you'll learn the latest developments in deep learning, how to read and implement new academic papers, and how to solve challenging end-to-end problems such as natural language translation. You'll develop a deep understanding of neural network foundations, the most important recent advances in the fields, and how to implement them in theworld's fastest deep learning libraries, fastai and pytorch.

Includes

Lecture 5: Lesson 12: Deep Learning Part 2 2018 - Generative Adversarial Networks (GANs)

4.1 ( 11 )


Lecture Details

NB: Please go to http://course.fast.ai/part2.html to view this video since there is important updated information there. If you have questions, use the forums at http://forums.fast.ai.

We start today with a deep dive into the DarkNet architecture used in YOLOv3, and use it to better understand all the details and choices that you can make when implementing a resnet-ish architecture. The basic approach discussed here is what we used to win the DAWNBench competition!

Then we’ll learn about Generative Adversarial Networks (GANs). This is, at its heart, a different kind of loss function. GANs have a generator and a discriminator that battle it out, and in the process combine to create a generative model that can create highly realistic outputs. We’ll be looking at the Wasserstein GAN variant, since it’s easier to train and more resilient to a range of hyperparameters.

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Sam

Excellent course helped me understand topic that i couldn't while attendinfg my college.

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Dembe

Great course. Thank you very much.

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