The pricey but standard recommendation would be buying and working through "Artificial Intelligence - A Modern Approach". It's one of my favourite IT books :)
There's a pretty fun/good course on one of the free MOOCs that pretty much follows the book and uses pacman as an example. It uses Python. It's the course that has a cute robot on the slides :)
Edit (found it, this one): https://www.edx.org/course/uc-berkeleyx/uc-berkeleyx-cs188-1...
Edit2: There's also this one but it's not the one I'm thinking of (it's also good though, Norvig is one of the authors of AI-AMA): https://www.udacity.com/course/cs271
Lecun's work on LeNet was pioneering, and basically set the template for deep learning. However, it's not as simple as him being "right all along"; incremental advances in neural net architectures, a lot of developments in stochastic gradient methods, and orders-of-magnitude improvements in hardware and data availability have been what made deep learning as powerful as it is today.
The main point is that skeptics in the 2000's were basically right about neural nets being of limited use, but other technologies advanced and broke down the barriers.
Lecun's original convolutional nets in 1998 were run on the then-gigantic dataset of 60,000 images. Consider that a company like Facebook can provide billions of images with some form of tagging, and you see the different world we live in.
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[ 3.8 ms ] story [ 27.2 ms ] threadThere's a pretty fun/good course on one of the free MOOCs that pretty much follows the book and uses pacman as an example. It uses Python. It's the course that has a cute robot on the slides :) Edit (found it, this one): https://www.edx.org/course/uc-berkeleyx/uc-berkeleyx-cs188-1...
Edit2: There's also this one but it's not the one I'm thinking of (it's also good though, Norvig is one of the authors of AI-AMA): https://www.udacity.com/course/cs271
The main point is that skeptics in the 2000's were basically right about neural nets being of limited use, but other technologies advanced and broke down the barriers.
Lecun's original convolutional nets in 1998 were run on the then-gigantic dataset of 60,000 images. Consider that a company like Facebook can provide billions of images with some form of tagging, and you see the different world we live in.
https://www.youtube.com/watch?v=7Pq-S557XQU