Deep Learning, Reinforcement Learning, and Neural Networks

Build drowsiness detection system, predict energy consumption, forecast weather with CNN, RNN, GRU, Keras, Tensorflow

What you'll learn
- Learn the basic fundamentals of deep learning, reinforcement learning, neural networks, and also getting to know their use cases
- Learn how to build drowsiness detection model using Convolutional Neural Networks and Keras
- Learn how to build drowsiness detection system using OpenCV
- Learn how to build traffic light colour detection model using Convolutional Neural Networks and Keras
- Learn how to build traffic light colour detection system using OpenCV
- Learn how to build maze solver using reinforcement learning
- Learn how to create maze using Pygame
- Learn how to build smart traffic light system using reinforcement learning
- Learn how to create traffic light simulation using Pygame
- Learn how to predict energy consumption using Multilayer Perceptron Regression
- Learn how to forecast weather using recurrent neural networks and gated recurrent unit
- Learn how to build handwritten digit recognition using artificial neural networks
- Learn how deep learning models work. This section covers input data, forward propagation, prediction output, loss calculation, backpropagation, and optimization
- Learn how reinforcement learning models work. This section covers environment observation, action selection, reward, penalty, policy update, continuous learning
- Learn how neural network models work. This section covers how input data flows through weighted connections and hidden layers

Requirements
- No previous experience in deep learning is required
- Basic knowledge in Python

Description
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 penalties for increasing vehicle waiting time and rewards for reducing the total number of stopped cars at the intersection. Then, in the third section, we are going to build neural network models, specifically, we are going to predict energy consumption using a Multi Layer Perceptron Regressor. This model will analyze historical data to forecast future energy demands which can help resource planning. Following that, we are also going to forecast weather and temperature using Recurrent Neural Networks and Gated Recurrent Unit. The system will capture sequential patterns in weather data to provide accurate short term forecasts. Lastly, at the end of the course, we are going to build a handwritten digit recognition system using Artificial Neural Networks. The user will be able to upload a handwritten digit image, and the system will be able to accurately classify the given digit.

Firstly, before getting into the course, we need to ask these questions to ourselves, why should we learn about deep learning, reinforcement learning, and neural networks? Why are they important? Well, here is my answer, deep learning can help to automatically extract complex patterns from large amounts of data, enabling us to make predictions with high accuracy. Reinforcement learning can help to develop systems that learn optimal behaviors through interaction and feedback from their environment. Meanwhile, Neural networks can help to build intelligent systems that learn in a way similar to humans and it can be used to solve a wide range of problems.

Below are things that you can expect to learn from this course:

Learn the basic fundamentals of deep learning, reinforcement learning, neural networks, and also getting to know their use cases

Learn how deep learning models work. This section covers input data, forward propagation, prediction output, loss calculation, backpropagation, and optimization

Learn how to build drowsiness detection model using Convolutional Neural Networks and Keras

Learn how to build drowsiness detection system using OpenCV

Learn how to build traffic light colour detection model using Convolutional Neural Networks and Keras

Learn how to build traffic light colour detection system using OpenCV

Learn how reinforcement learning models work. This section covers environment observation, action selection, reward, penalty, policy update, continuous learning

Learn how to build maze solver using reinforcement learning

Learn how to create maze using Pygame

Learn how to build smart traffic light system using reinforcement learning

Learn how to create traffic light simulation using Pygame

Learn how neural network models work. This section covers how input data flows through weighted connections and hidden layers, leading to predictions that are compared to the ground truth and refined through backpropagation

Learn how to predict energy consumption using Multilayer Perceptron Regression

Learn how to forecast weather using recurrent neural networks and gated recurrent unit

Learn how to build handwritten digit recognition using artificial neural networks

Who this course is for:
- Machine learning Engineers who are interested in building complex neural networks model using Keras and Tensorflow
- Software Engineers who are interested in building deep learning and reinforcement learning models
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