Code the ICML 2024 paper: "MADA: Meta-Adaptive Optimizers through hyper-gradient Descent"

Logistic Regression with different optimizers in Python from scratch

Implementations of main Machine Learning Agorithms from scratch: Gaussian Mixture Model, Gradient Boosting, Adam, RMSProp, PCA, QR, Eigendecomposition, Decision Trees etc.

Adam optimizer in Haskell using the StateT monad transformer.

From linear regression towards neural networks...

Deep-Learning-Optimization-Algorithms-Streamlit-Application

Differentially Private Gradient Descent Optimizers

Fully connected layer from scratch, with training on MNIST dataset

This script trains a convolutional neural network (CNN) to classify handwritten digits.

This work proposes a Sequential Motion Optimization with Short-term Adaptive Moment Estimation (SMO-SAdam) to train neural networks.

Artificial neural network package written in python

Implementation of my own optimization function in Keras to train a neural network. Also compared with common optimizers like ADAM.

Brain Tumor Detection using Deep learning.

A research project on enhancing gradient optimization methods

Model to predict bank customer churn

This repository contains a basic implementation of a feed forward neural network using TensorFlow and Keras to predict the onset of diabetes in Pima Indian women based on certain diagnostic measures. The dataset used for training and evaluation is the Pima Indians Diabetes Database, which is publicly available and widely used for machine learning

Coursework for the class ECE C147 (Neural Networks and Deep Learning)

LIBANN is a fast, portable and easy to use neural network library written in pure ANSI-C

Модуль 9. Підбір гіперпараметрів НМ. Глибоке навчання. Tensorflow. Keras.