Lesson 1: Practical Deep Learning for Coders 2022

Go to for code, notebooks, quizzes, etc. This course is designed for people with some coding experience who want to learn how to apply deep learning and machine learning to practical problems. There are 9 lessons, and each lesson is around 90 minutes long.

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

Thanks to bencoman, mike.moloch, amr.malik, and gagan on forums.fast.ai for creating the transcript.

Thanks to Raymond-Wu on forums.fast.ai for help with chapter titles.


Leave a Reply

Your email address will not be published.