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Machine Learning Engineer
trre. Transductive regular expressions
brzozowski. Brzozowski derivative python sketch
fslib. Finite state transducer library. Minimalistic pure C implementation.
cuda_autoencoder. Stacked autoencoder for sentiment analysis
attention_ocr. Python
matrixcalc. MIT IAP short course: Matrix Calculus for Machine Learning and Beyond
cproc. C compiler (mirror)
dstack. Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.
vim-metamath. vim mode for editing metamath files
tg. terminal telegram client
imgclsmob. Sandbox for training deep learning networks
catalyst. Accelerated deep learning R&D
pytorch-a2c-ppo-acktr-gail. PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
sru. Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)
piwise. Pixel-wise segmentation on VOC2012 dataset using pytorch.
rnn-benchmarks. Benchmarks for several RNN variations with different deep-learning frameworks
python_speech_features. This library provides common speech features for ASR including MFCCs and filterbank energies.
deep_q_rl. Theano-based implementation of Deep Q-learning
deep-q-learning. Jupyter Notebook
stanford-ctc. Neural net code for lexicon-free speech recognition with connectionist temporal classification
chainer. A flexible framework of neural networks for deep learning
cudamat. Python module for performing basic dense linear algebra computations on the GPU using CUDA.
trainingRNNs. Python
theano-rnn. Demonstration of recurrent neural network implemented with Theano
