I say, start with the FastAi course, it's great. Start somewhere, there isn't a defined roadmap for deep learning. Everybody has a different journey of learning these things. Also, start exploring the domains of deep learning problems, like computer vision and natural language processing. Find out what interests you the most and learn more about it.

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It depends on how in depth you would like to go. The intro to PyTorch on udacity is free and a great tool to learn how to start coding deep learning projects.

For a deeper understanding it is probably best to start at the basics of linear calculus (I’d recommend three blue one brown on YouTube), and understanding what happens to vectors visually when a transformation is applied.

The Coursera Deep Learning specialization is great. It starts with the basics, including a gentle introduction to the intuition behind the maths, then goes on to cover many important application areas. If you like a more structured approach (e.g. assignments, quizzes), then this is for you. It's quite a lot of work, but it will get you from completely clueless to comfortable with most of the concepts and ready to explore the field on your own.

I found the FastAI course too light on details and the Jupyter Notebook based deep learning framework they built abstracts too many details away ...and is yet another (not very popular / used in practice) framework to learn.

There's no way that you can understand DL unless you're proficient with some basic calculus (matrix multiplications, rank of a matrix, norms etc). You don't need to be good at math, but you really need to understand some concepts.

If you don't understand math, you won't improve, you will be just "imitating" people who know those concepts. You will be able to delay your complete failure, anyway.

The univeristy of Tübingen has really great courses for DL/ Math for DL (recently the essentials for Math have been posted) on YouTube. It convers the theory in much more depth than other courses I've seen online.

I heard good things about Andrew Ng's course. I will definitely check it out!

I started with FastAI because he said they'd go over the maths in the course and like I said it was something I was scared of. There's Khan Academy that has free courses for maths, which is pretty cool :D

I want to get good at it, not just copy paste things and hope it'll works.

I'm readdy to do whatever it takes :D I cannot get it out of my head lately. It's very exciting.

What maths concepts should I learn about aside from calculus?

Thank you :)

I'm constantly thinking about what you can with it. you can do almost everything. ChatGPT is crazy.

So much so stuff to learn, so much stuff to do lol. I wish I haven't wasted my early 20's playing games...

Thank you btw :)

I'm deciding over whether to take Andrew Ng's course first, or go to Khan Academy and get some math fundamentals first.

linear calculus? btw, that's an intersting channel :) thank you!

>univeristy of Tübingen

Another interesting one. Thank you for the recommendation! I got the all the time in the world to dig through those.

Hopefully I won't dissapoint you guy. Thanks for the help! :D

A lot of Stanford classes are free on youtube. They are probably among the best. Andrew Ng coursera classes are modified from his Stanford class. Most of math are not difficult, linear algebra, caculus and some statistics like maximum likelihood etc. Math can be more difficult if you want to study some branches of deep learning. The goal is to establish approximate functions with deep learning which is stacking up basic simple units into multiple layers of a deep network.

Andrew Ng's course is the best out there beginners. He dilutes the complexities and slowly teaches all the math. You don't even need any book. That being said, his cpurse is based on Tensorflow. I would suggest learning pytorch on the side.

To correct the other comment all the examples they mentioned are from linear algebra, though calculus is important too.

To understand the deep learning you’ll need linear algebra and multidimensional calculus. For some parts of deep learning you’ll also need probability & statistics knowledge.

CS231n(2016) : https://youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC

Updated and Expanded version of above: Michigan Deep learning for computer vision(2019): https://youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r

You can find the respective course website using google search. From there you can find the assignments and suggested readings for both of the above courses.

Well, I am not an expert on the subject, but per my experience you have to learn things when you need them. If you think you have to fulfill all the prerequisites before diving into deep learning, you will be stacking different kinds of courses above the two mentioned above. There will be a long list of courses before you are actually starting deep learning. So, just start, go through the readings, assignments and when stuck ask questions, search on Google, see parts of some other courses that are actually relevant.

Most DL books have an appendix with the linear algebra and calculus needed to understand what's in the book.

I've not seen it yet, but maybe you can take a look at the new course by Sebastian Raschka on the ligthning website?

I can't help with calcululs and algebra because I already know those subjects, so I can't tell you where to study such notions...

Thank you, but I think I've found a few resources for calculus and maths in general.

Again, Thank you :)