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100-days-of-ml-code

100-Days-Of-ML-Code中文版

batchbald

Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.

cuda-nnet

Spiking neural network implementation using CPU and CUDA engine

curiosity-is-all-you-need

PyTorch implementation of Curiosity Is All You Need

deeplearntoolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

episodic-curiosity

Tensorflow/Keras code and trained models for Episodic Curiosity Through Reachability

machinelearning

noreward-rl

[ICML 2017] TensorFlow code for Curiosity-driven Exploration for Deep Reinforcement Learning

pytorch-lif-autograd

shift-ai-models-to-real-world-products

Share some useful guides and references about how to shift AI models to real world products or projects.

slayerpytorch

PyTorch implementation of SLAYER for training Spiking Neural Networks

spike-response-model

Feedforward multilayer spiking neural network with SRM0 model. Gaussian receptive field has been used for information encoding

spiking-neural-network-snn-with-pytorch-where-backpropagation-engenders-stdp

What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.

spiking_relu_conversion

spytorch

Tutorial for surrogate gradient learning in spiking neural networks

statistical-learning-method_code

手写实现李航《统计学习方法》书中全部算法

stdp-mnist

tempotron

three-snn-learning-algorithms-in-brian2

The repository offer the simulation scripts of three different single-layer SNN learning algorihtms proposed by Querlioz, Diehl and this author.