Deep Learning, Reinforcement Learning, and Neural Networks

Welcome to Deep Learning, Reinforcement Learning, and Neural Networks course. This is a comprehensive project based course where you will learn how to build advanced artificial intelligence models using Keras, Tensorflow, Convolutional Neural Network, MLP Regressor, and Gated Recurrent Unit. This course is a perfect combination between Python and deep learning, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in machine learning. In the introduction session, you will learn the basic fundamentals of deep learning, reinforcement learning, and neural networks, additionally you will also get to know their use cases. Then, in the next section, you will learn how to find and download datasets from Kaggle, it is a platform that provides collections of high quality datasets from various sectors. Afterward, we will start the project. In the first section, we are going to build complex deep learning models, specifically, a driver drowsiness detection model using Keras and CNN. The system will be able to detect if the driver is drowsy and immediately give a warning on the screen. Following that, we are also going to build a traffic light detection model using Keras and CNN. This model will accurately identify the color of traffic lights in real time and if the detected color is red, it will display Stop, if the detected color is yellow, it will display Prepare to Stop and if the detected color is green, it will display Go. In the second section, we are going to build reinforcement learning models, starting with a maze solver using Q learning. The system will be able to learn optimal paths to efficiently solve the maze.The reward will be given when the agent reaches the goal, while penalties will be applied for hitting walls or taking longer paths. Additionally, we will develop a smart traffic light system using Q learning. This system will be able to intelligently manage traffic lights to reduce congestion and improve traffic flow. The agent will receive penaltie