Interactive Visual Machine Learning Demos.

[EMNLP'20][Findings] Official Repository for the paper "Why and when should you pool? Analyzing Pooling in Recurrent Architectures."

Deep Neural Networks for music genre classification as a proxy for multiple analytical studies

Multilayer Perceptron GAN, and two Convolutional Neural Network GANs for MNIST and CIFAR.

Adaptive-saturated RNN: Remember more with less instability

This repository helps in understanding vanishing gradient problem with visualization

Code repository for my CSU master's research on dead ReLU's

First public project written in Python i guess, anyway this is a repository for my class CS115.N12.KHCL

The vanishing gradient problem is a well-known issue in training recurrent neural networks (RNNs). It occurs when gradients (derivatives of the loss with respect to the network's parameters) become too small as they are backpropagated through the network during training.

Machine Learning Practical - Coursework 2: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during training. And exploring solutions using batch normalization and residual connections.

Machine Learning Glossary

Machine Learning Practical - Coursework 2 Report: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during training. And exploring solutions using batch normalization and residual connections.

I'll try to explain through the outcomes of Vanishing Gradient Problem