Essence
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Micrograd | 4,842 |  | 1 | a month ago | 1 | April 18, 2020 | 22 | mit | Jupyter Notebook | |
| A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API | ||||||||||
| Dynamic_neural_manifold | 117 | 4 years ago | other | Jupyter Notebook | ||||||
| Gran Dag | 74 | 3 months ago | 2 | mit | C++ | |||||
| Dagnet | 72 | a year ago | mit | Python | ||||||
| PyTorch code for ICPR 2020 paper "DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting" | ||||||||||
| Essence | 66 | 3 years ago | gpl-3.0 | Python | ||||||
| AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose. | ||||||||||
| Nnquery | 29 | 7 years ago | bsd-3-clause | Lua | ||||||
| Query complex neural network graph structures in Torch | ||||||||||
What I cannot create, I do not understand - Richard Feynman
essence
A directed acyclic computational graph builder, built from scratch on numpy and C, including auto-differentiation.
This was not just another deep learning library, its minimal code base was supposed to demonstrate how to:
- Build neural net modules.
- Put the modules together.
- Efficiently compute gradients from this design.
Demos
-
mnist-mlp.py: Depth-2 multi layer perceptron, with ReLU and Dropout; 95.3% on MNIST. -
lenet-bn.py: LeNet with Batch Normalization on first layer, 97% on MNIST. -
lstm-embed.py: LSTM on word embeddings for Vietnamese Question classification + Dropout + L2 weight decay. 85% on test set and 98% on training set (overfit). -
turing-copy.py: A neural turing machine with LSTM controller. Test result on copy task length 70:
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visual-answer.py. 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
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dqn-cartpole.py: 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.
License
GPL 3.0 (see License in this repo)