This repository contains all the Lab and Assignments from Andrew NG Machine Learning Specialization Course on Coursera.

Our Project generates Monet-style images using a Generative Adversarial Network (GAN) consisting of a generator and discriminator neural network. Also, It contains some additional features like Chatbot which tells you about what actually Monet-style images are, along with feature of Object Detection for generating its images using GAN architecture.

A pure implemention of BackPropagating artificial neutral net

reasrech on the best Machine Learning IDS model

This repository contains implementations of all Deep Learning Algorithms from scratch in Python. Mathematics required for DL and many projects have also been included. It also has practical tutorials on Generative AI.

Generating Shakespearean Text with LSTM Neural Networks: This project uses LSTM networks to generate text in the style of William Shakespeare. It explores the intersection of literature and AI by mimicking the rich linguistic nuances and poetic depth of Shakespearean prose and poetry.

This is an assignment from my Machine Learning for Mechanical Engineers course that demonstrates an understanding in binary classification using neural networks with scikit-learn and TensorFlow.

A Use Case of Neural Networks, classification of images: dogs and cats.

This repository contains multiple projects of my Bharat Intern Internship

ML, DL, Neural Network and some data scraping projects in here.

This project performs object recognition using CIFAR-10 and CNN/Random Forest models in Python. It preprocesses data, trains models, evaluates performance and compares results with the goal of high accuracy and feature importance understanding. The final output includes a comparison of the models and insights.

Implementing Neural Network from scratch (MLP and CNN), purely in numpy with optimizers

Implementing and experimenting with various classification techniques

Used a pre-trained model, ensemble duonet, to detect the type of emotion displayed by the image selected at random from a dataset consisting of 35,000 photographs. Provided a confusion matrix for error detection showing an accuracy of 74%.

Using TensorFlow and deep learning neural networks to analyze and classify the success outcome of charitable organizations.

A simple Neural Network model to detect Breast Cancer.

rnn lstm gru

Solution to freeCodeCamp.org Cat or Dog Image Classifier with Google Colaboratory

Predicting the number of daily rides in order to estimate the number of bikes to be maintained to facilitate bikers and avoid losing investment on extra unneeded bikes.

Theoretical project regarding hamiltonian and lagrangian neural network