This page is dedicated to showcasing various Deep Learning projects. Deep Learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn and make intelligent decisions on its own.

Projects Overview

Here are some of the Deep Learning projects that we have worked on:

  • Image Recognition: A project that uses Convolutional Neural Networks (CNNs) to classify images into different categories.
  • Natural Language Processing (NLP): A project that utilizes Recurrent Neural Networks (RNNs) to analyze and generate human-like text.
  • Reinforcement Learning: A project that explores the use of Q-Learning to train an agent to play a game.

Image Recognition

The Image Recognition project is designed to classify images into predefined categories. This is achieved by training a CNN on a large dataset of labeled images.

  • Dataset: CIFAR-10
  • Model: Convolutional Neural Network (CNN)

Example of Image Recognition

Natural Language Processing (NLP)

The NLP project focuses on understanding and generating human language. It uses RNNs to process sequences of text and generate coherent responses.

  • Application: Sentiment Analysis, Machine Translation
  • Model: Recurrent Neural Network (RNN)

Example of NLP in Action

Reinforcement Learning

The Reinforcement Learning project aims to train an agent to make decisions in an environment to maximize a reward signal.

  • Algorithm: Q-Learning
  • Environment: Atari Games

Example of Reinforcement Learning

For more information on Deep Learning projects and techniques, please visit our Deep Learning Resources.