How to start deep learning from scratch?

Submitted by Ok_Cartographer3000 t3_z15d5p in deeplearning

I graduated from a university as an electrical engineer. One of the courses was fuzzy logic using Matlab. After I graduated, I learned python by self-learning that's all.

I don't know much about machine learning but I want to start learning deep learning without the basics of machine learning.

I found deep learning in a python book by François Chollet and I started reading it. Is it okay to start like that? And if anyone has advice for me or a roadmap I will appreciate it.

Sorry for my bad english.

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Comments

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Nerveregenerator t1_ix94a1y wrote

Don’t overthink it. It takes a long time, so just do whatever interests you

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RichardBJ1 t1_ix9fcaw wrote

His book has some nice examples, works well. Really as the other answer has said though you need to follow your interests and apply those examples to something that interests you. Another idea is Kaggle; you can clone others code quite legitimately and understand what they were up to. So many examples on Kaggle you’ll surely find something that fits your interests!! Good luck

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SayOnlyWhatYouMeme t1_ixad67p wrote

I am still an amateur but I started with that book I think it's a great place to start. Also the tensorflow website has some good tutorials. Finally I would create a project for yourself and start building your own networks. That's what I did!

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q-rka t1_ixav4ld wrote

I have written some machine learning algorithms from scratch including neural network and CNNs and some of them might be in top 10 of Google search. If I have to start again, I woukd do follows:

  1. Learn Python and OOP.
  2. Master the NumPy and little of Pandas.
  3. Understanding equations and how to code them like matrix multiplications, dot product..
  4. Understanding of forward and back propagation and writing simple example in copy and trying to write code for it.
  5. Trying to solve XOR problem. Its fun!
  6. Learning about activation functions and how error propagates on different function and error functions.
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IshanDandekar t1_ixba9ql wrote

Also, see keras examples. They have great tutorials on how to solve various deep learning problems, which can give you a basic understanding on how you can approach projects.

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ItisAhmad t1_ixbjnf1 wrote

fast.ai and some good first issue on a good ML based repo.
That's it. that's the comment.

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drsimonz t1_ixbsnwm wrote

First you might want to start with Shallow Learning :D

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wisescience t1_ixbuzi7 wrote

If you’re a book learner, you might appreciate “Inside Deep Learning” by Edward Raff. Raff’s text uses PyTorch but really works at it from the ground up with math + code. Sebastian Raschka has some free online content as well, and his recent book covers DL from ch. 11+ — e.g., you’ll build a neural network from scratch and move on from there (also PyTorch-focused).

Glad to hear others’ comments as well as their reactions to these specific suggestions.

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treksis t1_ixcd92w wrote

Start with Andrew NG's DL course. This course is good for engineering dude. He doesn't dive super deep into the fundamentals but he helps you build minimum intuition to get the idea of what's going on behind DL. Reading heavy DL books at the beginning will make you feel exhausted.

You can also check out https://d2l.ai/ and video lectures for reference.

Also, if you really feel uncomfortable with all these DL library things __ init__super() thing with OOP stuff combined with tensors, Tensforflow, PyTorch and huggingface etc..., check out Andrej Karpathy's youtube channel.

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