You ask me so often for free online resources about deep learning that I decided to collect my favorite courses!
These topics interest you the most:
🟩 practical deep learning,
🟩 deep learning theory,
🟩 math resources to understand the two above.
Let's see them!
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1️⃣ Practical deep learning.
If you want to take a deep dive straight into the field and want to start training your models right away, hands down the best course for you out there is Practical Deep Learning for Coders by fast.ai. (course.fast.ai)
To move beyond training models and learn about tooling and infrastructure, IMO the best course for you is the Full Stack Deep Learning course by @full_stack_dl.
You can start these without any background in advanced mathematics. You'll even learn a lot on the way.
Both of these courses offer significant knowledge, and if you want to move more towards engineering, they cover all you need to know to start.
2️⃣ Deep learning theory.
If you want to see what is behind the curtain, my main recommendation is the second part of the fast.ai course: Deep Learning from the Foundations (course19.fast.ai/part2).
Another great source is the Stanford online course library.
If you are looking for more classical machine learning (like decision trees or Gaussian mixture models), the channel of Mathematical Monk is hands down my favorite resource.
A gem in this subject is the 4 chapters long mini-series of @3blue1brown about the inner workings of neural networks. This is worth watching even if you don't need to understand all the fine details.
Math lies at the very foundations of deep learning. Although advanced math is not necessary to be an expert (especially if you are more into engineering instead of data science), it can elevate your understanding to the next level.
However, this is probably the most difficult subject to begin with.
There are three pillars of machine learning math:
🟩 linear algebra,
🟩 multivariate calculus,
🟩 and probability.
My two favorite online resources are the Khan Academy and MIT OpenCourseWare lectures.
Khan Academy is geared towards more beginners, while the MIT courses go into the very fine details.
If you are new to math, I recommend starting with the Khan Academy lectures.
Have you ever thought about why neural networks are so powerful?
Why is it that no matter the task, you can find an architecture that knocks the problem out of the park?
One answer is that they can approximate any function with arbitrary precision!
Let's see how!
🧵 👇🏽
From a mathematical viewpoint, machine learning is function approximation.
If you are given data points 𝑥 with observations 𝑦, learning essentially means finding a function 𝑓 such that 𝑓(𝑥) approximates the given 𝑦-s as accurately as possible.
Approximation is a very natural idea in mathematics.
Let's see a simple example!
You probably know the exponential function well. Do you also know how to calculate it?
The definition itself doesn't really help you. Calculating the powers where 𝑥 is not an integer is tough.