Full Stack Deep Learning

Full Stack Deep Learning
Sergey Karayev, Josh Tobin, Pieter Abbeel

There are many great courses to learn how to train deep neural networks. However, training the model is just one part of shipping a deep learning project. This course teaches full-stack production deep learning:

  • Formulating the problem and estimating project cost
  • Finding, cleaning, labeling, and augmenting data
  • Picking the right framework and compute infrastructure
  • Troubleshooting training and ensuring reproducibility
  • Deploying the model at scale

The course is aimed at people who already know the basics of deep learning and want to understand the rest of the process of creating production deep learning systems. You will get the most out of this course if you have:

  • At least one-year experience programming in Python.
  • At least one deep learning course (at a university or online).
  • Experience with code versioning, Unix environments, and software engineering.

While we cover the basics of deep learning (backpropagation, convolutional neural networks, recurrent neural networks, transformers, etc), we expect these lectures to be mostly review.



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