Cutting Edge Deep Learning for Coders

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.

Beginner 0(0 Ratings) 0 Students enrolled English
Created by Admin corner
Last updated Wed, 08-Jun-2022
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Course overview
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.

Lesson 8 starts with a quick recap of what we’ve learned so far, and introduces the new focus of this part of the course: cutting edge research. We talk about how to read papers, and what you’ll need to build your own deep learning box to run your experiments. Even if you’ve never read an academic paper before, we’ll show you how to do so in a way that you don’t get overwhelmed by the notation and writing style. Another difference in this part is that we’ll be digging deeply into the source code of the fastai and Pytorch libraries: in this lesson we’ll show you how to quickly navigate and build an understanding of the code. And we’ll see how to use python’s debugger to deepen your understand of what’s going on, as well as to fix bugs.

The main topic of this lesson is object detection, which means getting a model to draw a box around every key object in an image, and label each one correctly. You may be surprised to discover that we can use transfer learning from an Imagenet classifier that was never even trained to do detection! There are two main tasks: find and localize the objects, and classify them; we’ll use a single model to do both these at the same time. Such multi-task learning generally works better than creating different models for each task—which many people find rather counter-intuitive. To create this custom network whilst leveraging a pre-trained model, we’ll use fastai’s flexible custom head architecture.

Curriculum for this course
7 Lessons 15:06:52 Hours
Lecture
7 Lessons 15:06:52 Hours
  • Single object detection
    Preview 02:01:15
  • Multi object detection
    02:05:33
  • NLP Classification and Translation
    02:07:55
  • Neural Translation
    02:15:58
  • Generative Adversarial Networks (GANs)
    02:17:55
  • Image Enhancement
    02:15:27
  • Super resolution; Image segmentation with Unet
    02:02:49
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