Geoff Hinton's legendary Coursera course on neural nets. It came out around the same time as AlexNet winning the ImageNet competition which sparked the current deep learning revolution.
At the time it was cutting edge to the point where he introduced a previously undescribed optimization method (RMSProp) that was subsequently used in papers, citing the lecture slides as their reference! But still accessible to anyone with basic college math. Of course it doesn't have any of the new stuff like transformers or diffusion models, but I still consider it as giving a good foundation for understanding backprop and neural nets.
Unlike every other AI course at the time it didn't try to teach you about all the other types of machine learning. Neural nets only. After taking it I was able to apply neural nets at work with pretty great results. Also, it gave me one of my favorite quotes: "To deal with hyper-planes in a 14-dimensional space, visualize a 3-D space and say 'fourteen' to yourself very loudly. Everyone does it."
Somewhere on Hinton's webpage, there's a series of mp4 lectures. They introduce general neural networks, and then lead into restricted boltzman machines, which he worked on. Probably one of the best introductions to RBMs.
At the time it was cutting edge to the point where he introduced a previously undescribed optimization method (RMSProp) that was subsequently used in papers, citing the lecture slides as their reference! But still accessible to anyone with basic college math. Of course it doesn't have any of the new stuff like transformers or diffusion models, but I still consider it as giving a good foundation for understanding backprop and neural nets.
Unlike every other AI course at the time it didn't try to teach you about all the other types of machine learning. Neural nets only. After taking it I was able to apply neural nets at work with pretty great results. Also, it gave me one of my favorite quotes: "To deal with hyper-planes in a 14-dimensional space, visualize a 3-D space and say 'fourteen' to yourself very loudly. Everyone does it."