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

>> 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.

For the 'backend' part:

Andrej Karpathy's Zero to Hero

Deep learning for coders part 1 (2022 edition).

Deep learning for coders part 2 (2022 edition), lectures 9-10 are available so far. The rest will apparently be published in a few weeks.

For prompt engineering:

Follow Riley Goodside on Twitter.

For frontend:

Build something with Streamlit or Gradio.

Try www.theschoolofai.in The live course happen only 2 times a year, and is one of the most rigorous out there.

They should ask chatGPT.

Try the fast.ai course

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