AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.
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What I cannot create, I do not understand - Richard Feynman


A directed acyclic computational graph builder, built from scratch on numpy and C, with auto-differentiation supported.

This was not just another deep learning library, its clean code base was supposed to be read. Great for any one who want to learn about Backprop design in deep learning libraries.


  • Depth-2 multi layer perceptron, with ReLU and Dropout; 95.3% on MNIST.

  • LeNet with Batch Normalization on first layer, 97% on MNIST.

  • LSTM on word embeddings for Vietnamese Question classification + Dropout + L2 weight decay. 85% on test set and 98% on training set (overfit).

  • A neural turing machine with LSTM controller. Test result on copy task length 70:


  • Visual question answering with pretrained weight from VGG16 and a stack of 3 basic LSTMs, on Glove word2vec.

Q: What is the animal in the picture?      . A: cat
Q: Is there any person in the picture?     . A: no
Q: What is the cat doing?                  . A: sitting
Q: Where is the cat sitting on?            . A: floor
Q: What is the cat color?                  . A: white
Q: Is the cat smiling?                     . A: yes
  • A classic solved with DQN, with experience replay and target network ofcourse. (Illustration below is one-take)

TODO: Memory network and GAN, for that I need to improve my speed of im2col and gemm for conv module first.


GPL 3.0 (see License in this repo)