Jeremy Howard provides this introductory lesson on Deep Learning for Coders.

In this first lesson, we learn about what deep learning is, and how it’s connected to machine learning, and regular computer programming. We get our GPU-powered deep learning server set up, and use it to train models across vision, NLP, tabular data, and collaborative filtering. We do this all in Jupyter Notebooks, using transfer learning from pretrained models for the vision and NLP training.

We discuss the important topics of test and validation sets, and how to create and use them to avoid over-fitting. We learn about some key jargon used in deep learning.

We also discuss how AI projects can fail, and techniques for avoiding failure.


  • 00:00 – Introduction
  • 06:44 – What you don’t need to do deep learning
  • 08:38 – What is the point of learning deep learning
  • 09:52 – Neural Nets: a brief history
  • 16:00 – Top to bottom learning approach
  • 23:06 – The software stack
  • 39:06 – Git Repositories
  • 42:20 – First practical exercise in Jupyter Notebook
  • 48:00 – Interpretation and explanation of the exercise
  • 55:35 – Stochastic Gradient Descent (SGD)
  • 1:01:30 – Consider how a model interacts with its environment
  • 1:07:42 – “doc” function and fastai framework documentation
  • 1:16:20 – Image Segmentation
  • 1:17:34 – Classifying a review’s sentiment based on IMDB text reviews
  • 1:18:30 – Predicting salary based on tabular data from CSV
  • 1:20:15 – Lesson Summary
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