Deep Learning for Coders with fastai and Pytorch

What Hacker News says

1. ericjmoreyDec 2023
   It's a shame that they don't include a chapter on ethics the way [Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD](https://course.fast.ai/Resources/book.html) does. I hope people read the etics chapter or use the ethics section of the [associated free course](https://course.fast.ai/) to fill in that oversight on the Amazon Engineers' part.

   Also it's a bit annoying that they address the fact that other resources exist but vaguely handwaved over the difference between their resources and the existing ones with "the others are not as up to date and don't have as much detail".

   Source: https://programming.dev/post/7017669

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2. rg111Feb 2023
   0. ML by Ng @Stanford

   1. Deep Learning Specialization by Ng @Coursera

   2. Deep Learning for Coders with fastai and PyTorch by Jeremy Howard

   3. Learn PyTorch really well. Either use d2l.ai or the PyTorch book from Manning.

       START MAKING PROJECTS HERE
   

   Now it depends where you want to go.

   >> Jump the hype train of LLM?

   1. Natural Language Processing specialization @Coursera

   2. Multilingual NLP from CMU

   3. Learn using HuggingFace with either their book or course.

   Your path from here will be visible to you.

   >> Jump the hype train of multimodal AI like Stable Diffusion, Dall-E, etc.?

   1. Diffusers course from HuggingFace.

   2. Plenty of resources out there in the forms of blogs, oss projects, etc.

   >> Want Edge AI, Federated Learning, or Deep RL?

   There are great resources like:

   1. Deep RL course from Hugging Face

   2. Deep Reinforcement Learning in Action by Zai, Brown

   3. Federated Learning course by Openmined

   4. Edge AI course from Harvard at edX

   5. Edge AI course from Edge Impulse

   There are also cool stuff happening in the Math+DL and Science+DL spaces. There are nice resources for those, too.

   A bit of advice, don't take too many courses and focus on building stuff, and take courses when you think that a gap exists in your knowledge.

   Deep Learning from NYU is taught by Yann LeCun and Alfredo Canziani, and it is pure gem.

   You will feel the need of learning math when you advance.

   Mathematics for ML from Imperial College London is great.

   So is Strang's Linear Algebra.

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3. ipnonSep 2022
   “Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD” will allow you to design and implement deep learning models with only good programming skill. Basic maths, no algorithms, no proofs. Howard’s paper on ULMFiT was state of the art 5 years ago, and it was originally just a part of this book. My claim is that this will be considered a classic in due time. It turns deep learning from an academic discipline into programming problems.
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4. rg111Feb 2022
   Math:

   After Sophomore year in High School, I realised that I have been doing Math all wrong. Although I always got scores close to 100%. I was just memorizing algorithms and formulas to solve problems. Something changed in me, and then I started asking- "why" and "how"? With the risk of sounding a little dramatic, this changed my life. I started to read Math for the first time in my life. I started to get them. There was a set of local textbook that contributed well in High School- Rudimentary Mathematics by four authors whose names I don't remember.

   In college, I decided to be a Physics major, and I had to take two Mathematical Physics classes. Big impact in my life. I mainly studied the textbook by Arfken, Weber, Harris, and Vector Calculus by Spiegel. Then, in that year and the next, I actually applied what I learned. That also made a big difference.

   I now work in Deep Learning. When I was learning Machine Learning and Deep Learning, my habit of properly understanding Math since the age of 16 paid me back very generously. I got the Math of Deep Learning instantly, with no struggles. Then I made projects and found employment. Here, too, I actually applied what I learned.

   I see being able to apply Math in your job as a sign of being fortunate and I am grateful.

   Anyway, I would advise you learn Math with a purpose. Rather just learning Math for tests or math's sake, learn Math with a purpose. Do something with the math you learn. It may be Data Science, may be computer graphics, may be cryptography, maybe anything. This will make a huge change in how you see math.

   Watching MIT OCW Calculus course videos in HS had a big influence in me.

   CS:

   I would say that learning to program well made the field worthwhile for me.

   Others have mentioned Code by Petzold.

   When I was learning Deep Learning, Jeremy Howard's book "Deep Learning for Coders with fastai and PyTorch" and the lecture videos made Deep Learning easier for me. I did not rely upon only that. But it was a great starting point.

   The Little Schemer was great, too. This little book is worth much more than one who hadn't read it might think.

   The C Programming Language by Kernighan and Ritchie is one of the books I read early in life. Did not finish it. Still, a great book.

   I always feared the breadth of Algorithm books. "A Common Sense Guide to Data Structures and Algorithms" fro PragProg Bookshelf gave me very nice overview of DSA.

   Python Crash Course by Eric Matthes made programming click for me when I was in my second year in college.

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5. JugurthaFeb 2021
   I think Andrew Ng's course on Coursera is targeted towards a different audience. You are pretty good with math and I think you'll appreciate his CS229 Stanford course more, as he goes through the math, and does right off the bat on the blackboard/whiteboard. He goes covers gradient descent in the first lecture [after the intro lecture]. 2008[0] and 2018[1] versions.

   Yaser Abu-Mostafa has CS156 at Caltech, a course and a textbook called "Learning from Data"[2][3].

   It depends where you want to go. If you want to learn Python libraries like Tensorflow and Pytorch, Keras, and the like... there are many books for that.

   "Deep Learning with Python", by François Chollet.[4][5]

   "Data Science from Scratch: First Principles with Python", by Joel Grus.[6][7]

   There are the fast.ai courses on https://fast.ai and also have a book titled:

   "Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD" by Jeremy Howard and Sylvain Gugger. They have the lectures for several years on machine learning and deep learning.

   There are many books and resources on how to train models. However, there are extremely few that enable you to do ML in a non toy project setting and do useful things in the real world.

   One recent course is CS329s "Machine Learning Systems"[8] that fits nicely after CS229. I only skimmed some slides, but it appears to be interesting.

   Disclaimer: Yes many publications about deployment, and ML project lifecycle management and platforms, but I have blocked most of them, including the largest publication about data science and machine learning on Medium from search results in my browser, because it publishes garbage posts from people who seem to never have worked on a project with actual stakes.

   I know it was not your question, but I guess you'd like to do something with ML in the real world. There is a range of skills and process necessary to make things happen. Identifying the right problem, designing the right metrics (tricky, especially when working with a project involving "intangibles"). Reframing. These also require interviewing skills and deep dives into a subject matter (so the ability to discover and curate the right resources, then read and synthesize) to be able to interact with domain experts and see a project through.

   - [0]: https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599

   - [1]: https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXSh...

   - [2]: https://www.youtube.com/playlist?list=PLD63A284B7615313A

   - [3]: https://work.caltech.edu/telecourse

   - [4]: https://www.manning.com/books/deep-learning-with-python

   - [5]: https://github.com/fchollet/deep-learning-with-python-notebo...

   - [6]: https://www.oreilly.com/library/view/data-science-from/97814...

   - [7]: https://github.com/joelgrus/data-science-from-scratch

   - [8]: https://stanford-cs329s.github.io/syllabus.html

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6. westurnerAug 2022
   "Practical Deep Learning for Coders" 2022: https://github.com/fastai/course22

   "Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD" (2020) https://github.com/fastai/fastbook

   There's also a new nbdev; nbdev v2: https://github.com/fastai/nbdev and nbdev-template to start new projects with: https://github.com/fastai/nbdev-template

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