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

A collection of deep learning exercises collected while completing an Intro to Deep Learning course. We use TensorFlow and Keras to build and train neural networks for structured data.

A simple study on how to use Tensorflow platform (without Keras) for a simple number classification task using a Neural Network.

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.

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

A quantitative measure of disease progression one year after baseline

Recurrent neural network with GRUs for trigger word detection from an audio clip

Predicting Meta stock prices using MLP, RNN and LSTM models.

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.

Predicting Turbine Energy Yield (TEY) using ambient variables as features.

Deep Learning models

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

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

A Image classification CNN model with more than 85% accuracy. An interactive API is been designed using flask framework for better user experience. Techniques like batch normalization, dropouts is used for improved accuracy.

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

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

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

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

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.

Model Optimization using Batch Normalization and Dropout Techniques