rmsprop

Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games

A tour of different optimization algorithms in PyTorch.

The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.

[Python] [arXiv/cs] Paper "An Overview of Gradient Descent Optimization Algorithms" by Sebastian Ruder

SC-Adagrad, SC-RMSProp and RMSProp algorithms for training deep networks proposed in

XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.

Short description for quick search

Hands on implementation of gradient descent based optimizers in raw python

Hopfield NN, Perceptron, MLP, Complex-valued MLP, SGD RMSProp, DRAW

Term project for CAP6619 Deep Learning class, Fall 2018 - dropout vs. batch normalization

Python library for neural networks.

A Repository to Visualize the training of Linear Model by optimizers such as SGD, Adam, RMSProp, AdamW, ASMGrad etc

Library which can be used to build feed forward NN, Convolutional Nets, Linear Regression, and Logistic Regression Models.

Gradient descent optimizers for linear regression

An OOP Deep Neural Network using a similar syntax as Keras with many hyper-parameters, optimizers and activation functions available.

Fully connected neural network for digit classification using MNIST data

A collection of various gradient descent algorithms implemented in Python from scratch

Aibrite Machine Learning Framework

Curso Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. Segundo curso del programa especializado Deep Learning. Este repositorio contiene todos los ejercicios resueltos. https://www.coursera.org/learn/neural-networks-deep-learning

Researching stochastic gradient descent methods based on the scientific paper

Implemented fully-connected DNN of arbitrary depth with Batch Norm and Dropout, three-layer ConvNet with Spatial Batch Norm in NumPy. The update rules used for training are SGD, SGD+Momentum, RMSProp and Adam. Implemented three block ResNet in PyTorch, with 10 epochs of training achieves 73.60% accuracy on test set.

A Siamese Neural Network is a class of neural network architectures that contain two or more identical subnetworks. ‘identical’ here means, they have the same configuration with the same parameters and weights.

Notebooks with various models for images recognition based on dices example

Deep Learning Optimizers

Comparing different parameter settings for deep convolution neural network for image classification

A five-course specialization covering the foundations of Deep Learning, from building CNNs, RNNs & LSTMs to choosing model configurations & paramaters like Adam, Dropout, BatchNorm, Xavier/He initialization, and others.

A C# WGAN.