These are codes i used to learn tensorflow basics. I've been using caffee and torch before and the leap from there to TF was quite fascinating.
Hope it helps someone else too for reference.
The codes I cover are:
1. Basic FC network with dropouts and L2 regularization added
2. Basic Convnets
3. RNN/LSTM, Bi-LSTM,dynamic rnn, etc..
4. Fine Tuning Alexnet
5. Different types of GANs
6. Autoencoders / VAE
7. Batch Normalization
8. Seq2Seq models
9. TF specific utils like TFRecords, Tensorboard, while_loop, dynamic calculation techniques etc.
10. Deep Recurrent Attentive Writer
11. Neural Turing Machine/ Differentiable Neural Computers
12. Capsule Nets
13. SNN/Selu
14. Hierarchichal MultiScale RNN