"# AutoEncoders"

1. Internet Ads Detection with Autoencoders:

   Detecting internet ads using an Autoencoder-based machine learning model. The Internet Ads Detection project aims to identify and classify internet ads using Autoencoders, which are a type of artificial neural network. By training an Autoencoder on a dataset of internet ads, the model learns to reconstruct the input data, effectively identifying anomalies, which in this case are non-ads or malicious content. This project demonstrates the use of Autoencoders for anomaly detection, and it can be valuable for identifying and filtering out unwanted content from websites and online

2. Movie Recommendation System with Autoencoders:

A movie recommendation system implemented using Autoencoders, which are a type of artificial neural network. This system provides movie recommendations based on a user's preferences and movie ratings. The Movie Recommendation System is designed to provide movie recommendations to users based on their historical movie ratings and preferences. It utilizes Autoencoders to learn patterns in user-movie
interactions and provide personalized movie recommendations. This project serves as a demonstration of collaborative filtering using neural networks.

3. Keras Autoencoder for Image Denoising:

   A Keras-based autoencoder for image denoising, which can remove noise from images. This project demonstrates how to build an autoencoder model for image reconstruction. The Keras Autoencoder for Image Denoising is a neural network model designed to remove noise from images. Autoencoders consist of an encoder that compresses the input image into a low-dimensional representation and a decoder that reconstructs the clean image from this representation. This project serves as a hands-on example of building an image