Overview

The Machine Learning Gallery is a project that showcases various experiments with neural networks. It is designed to help beginners understand the concepts of machine learning through visualizations. Users can train and run neural networks live and see the results for themselves. Each project is accompanied by an explanation of how it works. The majority of the models are trained with PyTorch on a Django backend server, while the frontend is a React app that connects to the backend using Websocket. Some larger models are pre-trained. The technologies used in the project include PyTorch, React, and TensorFlow JS. The project is deployed at https://akhil.ai.

Features

Feed-Forward Networks: Learn the concepts of feed-forward networks.
Linear Classifier: Train a linear classifier to classify data.
Polynomial Curve Learning: Learn to fit a curve using polynomial regression.
Deep Iris: Understand the concept of computer vision and train a network for iris recognition.
Character Identification: Train a network to identify characters.
MNSIT GAN: Generate images of handwritten digits using a generative adversarial network (GAN).
Image Colorization: Colorize black and white images.
Number Recognition: Train a network to recognize and identify numbers in images.
Attention Models: Learn about attention mechanisms in neural networks.
Style Transfer: Transfer the style of one image onto another using deep learning.
Natural Language Processing: Learn about word embeddings and text classification.
Reinforcement Learning: Train agents to play games like TicTacToe, Ping-Pong, and Racer.
Unsupervised Learning: Learn about autoencoders and self-organizing feature maps.
Memorization: Train a network to memorize and recall patterns.
Spiking Neurons: Learn about spiking neural networks.
MNIST Detection Dataset: Use a pre-trained model to detect objects in the MNIST dataset.

Summary

The Machine Learning Gallery is a project that provides a platform for beginners to learn and explore machine learning concepts through visualizations. It offers a range of projects that cover various topics such as feed-forward networks, computer vision, natural language processing, and reinforcement learning. The gallery uses PyTorch for training models on a Django backend server and connects to a React frontend app. Users can train and run the networks live, with each project accompanied by an explanation of how it works. The gallery is a valuable resource for those looking to gain practical experience in machine learning.

Akhilez

Ml_gallery