Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

Educational deep learning library in plain Numpy.

A tour of different optimization algorithms in PyTorch.

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

A compressed adaptive optimizer for training large-scale deep learning models using 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

From linear regression towards neural networks...

implementation of factorization machine, support classification.

Simple MATLAB toolbox for deep learning network: Version 1.0.3

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

Hands on implementation of gradient descent based optimizers in raw python

Implementation of Convex Optimization algorithms

Song lyrics generation using Recurrent Neural Networks (RNNs)

Python library for neural networks.

a python script of a function summarize some popular methods about gradient descent

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

Gradient_descent_Complete_In_Depth_for beginners

Repository for machine learning problems implemented in python

in this repository we intend to predict Google and Apple Stock Prices Using Long Short-Term Memory (LSTM) Model in Python. Long Short-Term Memory (LSTM) is one type of recurrent neural network which is used to learn order dependence in sequence prediction problems. Due to its capability of storing past information, LSTM is very useful in predict…