Cutting-Edge AI: Deep Reinforcement Learning in Python
$109.99 Track price
Welcome to Cutting–Edge AI!
This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course.
Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks).
While both of these have been around for quite some time, it’s only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning.
The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer.
Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be.
We’ve seen how AlphaZero can master the game of Go using only self–play.
This is just a few years after the original AlphaGo already beat a world champion in Go.
We’ve seen real–world robots learn how to walk, and even recover after being kicked over, despite only being trained using simulation.
Simulation is nice because it doesn’t require actual hardware, which is expensive. If your agent falls down, no real damage is done.
We’ve seen real–world robots learn hand dexterity, which is no small feat.
Courses : 22
Specification: Cutting-Edge AI: Deep Reinforcement Learning in Python
6 reviews for Cutting-Edge AI: Deep Reinforcement Learning in Python
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Anton Jing –
The courses are well structured and I would highly advocate for students to take each one in sequence if you are interested in becoming an expert in reinforcement learning. You can build intuition and develop a hands on approach.
Redemptive Dialectic –
This is more than exactly what I was looking for, however he does smack talk philosophy (love of wisdom) for a moment even though computer science and artificial intelligence originated from philosophy and the mathematics here are mostly approximations rather than exact answers. Overall though this course is still significantly better than 99% of what you can find out there. Keep up the good work.
Konstantin Borozdin –
good course so far
Kyle Miller –
Not a criticism of the course really but the last sections on coding and background of AI were not useful to me.
Naman Chaturvedi –
lacks the fundamental premise of the A2C .. too focused on code structure.
Odai Aldawoud –
I’ve learned material I would have never thought I would ever understand. I am grateful for all the courses created… The theory is supported by code, and the code is extremely logical and easy to read (which solidifies the theory!!!) This is too great