Welcome to 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