Practical Deep Learning for Coders: Lesson 1

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

[IbrahimSowunmi]

Compiling how I fixed this.
1) In the bottom right hand corner open up the notebook. Change the environment from "pin to the version" to "Always use latest environment".
2) ddg_images has been deprecated -

from duckduckgo_search import DDGS

def search_images(keywords, max_images = 30):
print(f"Searching for {keywords}")
return L(DDGS().images(keywords, max_results=max_images)).itemgot('image')

Use this function instead. Like for visibility.

[isaacfernandez]

Sir. I remember back in the day I wanted a bachelor in data science and started reading your books. Now I have been admitted to a graduate program. Thank you, you are doing a lot for this field.

[wolktm]

Sir, you thought me 20 years ago when I was studying at QUT. Great to see you are still teaching - you have a great talent at that! All best, greatings from Poland!

[beautifulsmall]

As a middle aged hardware enginner I went to a ML workshop at work which started with, "So is everyone familiar with matracies". All the graduates nodded. So fast ai is a tool that looks hugley benificial . Cloud based jupyter notebook is a big nono for industry security though so im running in pycharm which isn't so straightforward but works so far. Many thanks for this development.

[shoaibshafiq]

No Teacher has got me interested into a course this much only after first Lesson
This was packed with so much information but presented in such a good way that it felt like I am reading a children book.

[MrYamashici]

Insane this knowledge is out there for free. Thank you so much Jeremy, and everyone that made this possible!

[shaunrinse]

Hats off to you Jeremy, and everyone at Fastai. Over the years your course has improved and improved, and today it is truly a well oiled machine. Keep it free, keep it democratic.

[zzznavarrete]

This is pure gold, thanks Jeremy for put so much effort in give a comprehensive education to the world in such important topic

[helloworldcsofficial]

You are the best. Thank you for this course! Hope you update your book in the future so that we all can keep up with the latest topics in this field.

[kevinbacon]

You sir are a saint. My adhd rarely lets me truly focus on a video lecture, but you had me dialed in. Thank you. I am looking forward the rest of the course videos.

[s.dotmedia]

From the bottom of my ad agency just-get-it-out-the-door developer's heart, thank you sir, for your pragmatism and amazing instructional style. This is the course I needed to connect my world to AI, your changing lives my friend!

[dmitrymitrofanov]

just finished first home work.Thank you!

[antonioalvarado]

Freedom for deep learning: Unlocked. Thank you sir.

[ramirocaro]

Amazing as always, it's the third time i do the course, and I learn new stuff every-time! Thanks a lot for this invaluable resource!

[vivekagrawal]

Happy 1st Birthday! <3 Will be building a startup from this learning

[UdayGarg]

Let's goooo!!
You are a god for doing this for free jeremy. Thank you so much.

[dennisash]

Oh I am super happy that you are doing this, I loved the course 2 years ago and I have benefitted hugely. I am helping to educate others and will definitely be enjoying this course with you.

[openaidalle]

I did the v2 course, now using these to teach my students. Thank you so much..

[balajicherukuri]

love it! first time ever understand what is ml, of course at surface level. thank you

[IsxaaqAcademy]

You are too generous to put such great content in YT for free!