A collection of LightGBM callbacks. (DART early stopping, tqdm progress bar)

A box of tools that deal with numbers.

Building Early Stopping mechanism using PyTorch

Project that detects the brand of a car, between 1 and 49 brands, that appears in a photograph, with a success rate of more than 70% (using a test file that has not been involved in the training as a valid or training file, "unseen data") and can be implemented on a personal computer

Utilizing advanced Bidirectional LSTM RNN technology, our project focuses on accurately predicting stock market trends. By analyzing historical data, our system learns intricate patterns to provide insightful forecasts. Investors gain a robust tool for informed decision-making in dynamic market conditions. With a streamlined interface, our solution

SPECTRA: Solar Panel Evaluation through Computer Vision and Advanced Techniques for Reliable Analysis

Tomato Leaf Disease Detection:Deep Learning Project

A Deep Learning model for California housing dataset using Functional API with Wide & Deep neural network architecture along with ModelCheckpoint and EarlyStopping callbacks.

implementing AdaBoost from scratch and comparing it with Scikit-Learn's implementation along with exploring concept of early stopping and weighted errors in boosting algorithms.

A CNN model to identify images of plant seedlings.

Enhance medical diagnostics with our CNN-powered X-Ray Image Classifier, accurately identifying Covid-19, Normal, and Viral Pneumonia cases for proactive patient care.

The objective of this repository is to provide a learning and experimentation environment to better understand the details and fundamental concepts of neural networks by building neural networks from scratch.

CIFAR10 Dataset.

This repository contains my code solutions to Udacity's coursework 'Intro to Deep Learning with PyTorch'.

Early stopping for PyTorch

A Fork of "Early stopping for PyTorch" with a whl file

Used a Multilayer Perceptron (MLP) neural network to detect COVID-19 in lung scans.

Predicting a FIFA player's playing position based on their skills using artificial neural networks.

Time series analysis of SINE wave using Recurrent neural network.

I implemented a CNN to train and test a handwritten digit recognition system using the MNIST dataset. I also read the paper “Backpropagation Applied to Handwritten Zip Code Recognition” by LeCun et al. 1989 for more details, but my architecture does not mirror everything mentioned in the paper. I also carried out a few experiments such as adding…