[Work in Progress] Forked for Dropout Mechanism Development

Python from-scratch implementation of a Neural Network Classifier

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

This repository provides a simple implementation of churn prediction using Artificial Neural Networks for beginners in deep learning.

The aim was to develop a robust Convolutional Neural Network (CNN) for accurately classifying handwritten digits from the MNIST dataset

The primary objective of this project is to design and train a deep neural network that can generalize well to new, unseen data, effectively distinguishing between rocks and metal cylinders based on the sonar chirp returns.

Neural Network

Annotated vanilla implementation in PyTorch of the Transformer model introduced in 'Attention Is All You Need'

Translates the live video feed from opencv into text format and displays this onto the frame. Uses LSTM, Dropouts, Regularizers and Learning Rate Scheduler

BabyGPT: Build Your Own GPT Large Language Model from Scratch Pre-Training Generative Transformer Models: Building GPT from Scratch with a Step-by-Step Guide to Generative AI in PyTorch and Python

Deep Learning project about the design and training of a model for Image Classification

This library provides a set of basic functions for different type of deep learning (and other) algorithms in C.This deep learning library will be constantly updated

To provide a complete pipeline to develop a deep learning model. More specifically, a multiclass classification (single label) deep learning model that can predict what stage of Alzheimer's a patient is, from their MRI image

ANN model to predict customer churn based on some information about the customer and used Dropout regulization to avoid overfitting in my model.

Model Optimization using Batch Normalization and Dropout Techniques

This GitHub repository explores the importance of MLP components using the MNIST dataset. Techniques like Dropout, Batch Normalization, and optimization algorithms are experimented with to improve MLP performance. Gain a deeper understanding of MLP components and learn to fine-tune for optimal classification performance on MNIST.

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.

This project aims to build an Multivariate time series prediction LSTM model to predict the stock price.

Implement GAN (Generative Adversarial Network) on MNIST dataset. Vary the hyperparameters and analyze the corresponding results.

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.