Welcome to Deep Learning for Coders! Be sure to watch these videos through https://course.fast.ai to get access to the searchable transcript, interactive notebooks, setup guides, questionnaires, and so forth. We don't recommend watching the videos directly on YouTube.

 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

Caption author (Spanish)


Caption author (Turkish)


Caption author (Bulgarian)

Krasin Georgiev

Caption author (Chinese (Simplified))

Ruyuan (Tricia) Wan

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