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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
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%.
Theoretical project regarding hamiltonian and lagrangian neural network
rnn lstm gru
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.
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.
A simple Neural Network model to detect Breast Cancer.
A Use Case of Neural Networks, classification of images: dogs and cats.
Implementing Neural Network from scratch (MLP and CNN), purely in numpy with optimizers
ML, DL, Neural Network and some data scraping projects in here.
Using TensorFlow and deep learning neural networks to analyze and classify the success outcome of charitable organizations.
Implementing and experimenting with various classification techniques
This repository contains multiple projects of my Bharat Intern Internship