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.
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!
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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.