The fast.ai course does what it says on the tin: it's a practical approach to deep learning. I know I'm being lame by copying their tagline, but it really does just that: it has a no-frills approach to _applying_ deep learning. What you'll see:
* How to build and train a convnet
* What transfer learning entails
* How to build and train an LSTM
* How to build and train a 'vanilla' neural net
What you won't be covering:
* The difference between ADAM and EVE optimizers
* The mathematics behind backpropagation
* The mathematical 'theory' behind 'exploding gradients'
In brief: fast.ai is all about having coders get started with deep learning ASAP. If you have theoretical questions, the answer will usually be a one-liner, along with "but that's out of scope for this class".
I loved it to pieces, I think it's fantastic and a must-do if you've got any Python affinity. You would not believe what you, a run-off-the-mill programmer, have as a power when it comes to getting ConvNets/Nnets/LSTMs do. You can really build powerful, (almost) Google service level stuff.
* How to build and train a convnet
* What transfer learning entails
* How to build and train an LSTM
* How to build and train a 'vanilla' neural net
What you won't be covering:
* The difference between ADAM and EVE optimizers
* The mathematics behind backpropagation
* The mathematical 'theory' behind 'exploding gradients'
In brief: fast.ai is all about having coders get started with deep learning ASAP. If you have theoretical questions, the answer will usually be a one-liner, along with "but that's out of scope for this class".
I loved it to pieces, I think it's fantastic and a must-do if you've got any Python affinity. You would not believe what you, a run-off-the-mill programmer, have as a power when it comes to getting ConvNets/Nnets/LSTMs do. You can really build powerful, (almost) Google service level stuff.
But it's not very detailed on theory.