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

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

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.

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.

In this repository I have included all the ipynb files in which I have tried to implement the neural network and other concepts from scratch.

in this repo, you will find implementation of various classification models, data augmantation ,cnn designing and model reguralization

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

[Work in Progress] Forked for Dropout Mechanism Development

Deep Learning models

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

A study of the use of the Tensorflow GradientTape class for differentiation and custom gradient generation along with its use to implement a Deep-Convolutional Generative Adversarial Network (GAN) to generate images of hand-written digits.

Model Optimization using Batch Normalization and Dropout Techniques

Neural Network

A beginner's investigation into the world of neural networks, using the MNIST image dataset

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

Implementation of CNN (consisting of maxpool, relu, fully-connected and convolutional layers) using Numpy Vectorisation (from scratch without any third-party library), followed by analysis using hyperparameter tuning and different regularisation techniques

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

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

Fall 2021 Introduction to Deep Learning - Homework 1 Part 2 (Frame Level Classification of Speech)

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