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

Keras/TF implementation of AdamW, SGDW, NadamW, Warm Restarts, and Learning Rate multipliers

Hands-On Deep Learning Algorithms with Python, By Packt

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

From linear regression towards neural networks...

A comparison between implementations of different gradient-based optimization algorithms (Gradient Descent, Adam, Adamax, Nadam, Amsgrad). The comparison was made on some of the most common functions used for testing optimization algorithms.

Gradient_descent_Complete_In_Depth_for beginners

"Simulations for the paper 'A Review Article On Gradient Descent Optimization Algorithms' by Sebastian Roeder"

This project focuses on land use and land cover classification using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The classification task aims to predict the category of land based on satellite or aerial images.

Фреймворк глубоко обучения на Numpy, написанный с целью изучения того, как все работает под "капотом".

A deep learning classification program to detect the CT-scan results using python

A news article's title and description should be classified into the following groups in order to solve this classification problem: 1-World, 2-Sports, 3-Business and 4-Science/Tech .Here is a sequence of data. This is a sequential problem, thus we may use bidirectional LSTM for classification since we have access to the data.

Analyze the performance of 7 optimizers by varying their learning rates

Assignment submission for the course Fundamentals of Deep Learning (CS6910) in the Spring 2022 Semester, under Prof. Mitesh Khapra

Data Structures, Algorithms and Machine Learning Optimization

This repository contains a python implementation of Feed Forward Neural Network with Backpropagation, along with the example scripts for training the network to classify images from mnist and fashion_mnist datasets from keras.