GitHub - rahulchandrangb/DL-TF: Boxing with tensorflow .. Cheers!!

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

Name		Name	Last commit message	Last commit date
Latest commit History 44 Commits
New_nets		New_nets
Paper Reads		Paper Reads
helping_utils		helping_utils
out_of_syllabus/Finetuning_tuts		out_of_syllabus/Finetuning_tuts
transformer		transformer
1- DataLoader Scripts.ipynb		1- DataLoader Scripts.ipynb
1- Softmax.ipynb		1- Softmax.ipynb
3-Regularization+dropout.ipynb		3-Regularization+dropout.ipynb
4- Basic Convnets.ipynb		4- Basic Convnets.ipynb
Autoencoder.ipynb		Autoencoder.ipynb
Basics of TF-1.ipynb		Basics of TF-1.ipynb
Bi-Directional LSTM.ipynb		Bi-Directional LSTM.ipynb
Differentiable Neural Computer(DNC).ipynb		Differentiable Neural Computer(DNC).ipynb
Draw-Deep Recurrent Attentive Writer.ipynb		Draw-Deep Recurrent Attentive Writer.ipynb
GAN-1.ipynb		GAN-1.ipynb
GAN-2-cGAN.ipynb		GAN-2-cGAN.ipynb
README.md		README.md
Self-Attention-Transformer.ipynb		Self-Attention-Transformer.ipynb
Self_Normalizing_Networks_and_Selu.ipynb		Self_Normalizing_Networks_and_Selu.ipynb
Seq2Seq.ipynb		Seq2Seq.ipynb
Stacked Bidirectional LSTM.ipynb		Stacked Bidirectional LSTM.ipynb
Word2Vec.ipynb		Word2Vec.ipynb
ae.gif		ae.gif
lstm-1.ipynb		lstm-1.ipynb
lstm.ipynb		lstm.ipynb
lstm.png		lstm.png

rahulchandrangb/DL-TF

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