Lesson 1 Practical Deep Learning for Coders 2022

We cover topics such as how to:
- Build and train deep learning, random forest, and regression models
- Deploy models
- Apply deep learning to computer vision, natural language processing, tabular analysis, and collaborative filtering problems
- Use PyTorch, the world’s fastest growing deep learning software, together with popular libraries such as fastai, Hugging Face Transformers, and gradio

You don’t need any special hardware or software — we’ll show you how to use free resources for both building and deploying models. You don’t need any university math either — we’ll teach you the calculus and linear algebra you need during the course.

00:00 - Introduction
00:25 - What has changed since 2015
01:20 - Is it a bird
02:09 - Images are made of numbers
03:29 - Downloading images
04:25 - Creating a DataBlock and Learner
05:18 - Training the model and making a prediction
07:20 - What can deep learning do now
10:33 - Pathways Language Model (PaLM)
15:40 - How the course will be taught. Top down learning
19:25 - Jeremy Howard’s qualifications
22:38 - Comparison between modern deep learning and 2012 machine learning practices
24:31 - Visualizing layers of a trained neural network
27:40 - Image classification applied to audio
28:08 - Image classification applied to time series and fraud
30:16 - Pytorch vs Tensorflow
31:43 - Example of how Fastai builds off Pytorch (AdamW optimizer)
35:18 - Using cloud servers to run your notebooks (Kaggle)
38:45 - Bird or not bird? & explaining some Kaggle features
40:15 - How to import libraries like Fastai in Python
40:42 - Best practice - viewing your data between steps
42:00 - Datablocks API overarching explanation
44:40 - Datablocks API parameters explanation
48:40 - Where to find fastai documentation
49:54 - Fastai’s learner (combines model & data)
50:40 - Fastai’s available pretrained models
52:02 - What’s a pretrained model?
53:48 - Testing your model with predict method
55:08 - Other applications of computer vision. Segmentation
56:48 - Segmentation code explanation
58:32 - Tabular analysis with fastai
59:42 - show_batch method explanation
1:01:25 - Collaborative filtering (recommendation system) example
1:05:08 - How to turn your notebooks into a presentation tool (RISE)
1:05:45 - What else can you make with notebooks?
1:08:06 - What can deep learning do presently?
1:10:33 - The first neural network - Mark I Perceptron (1957)
1:12:38 - Machine learning models at a high level
1:18:27 - Homework