Udemy – 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.

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

DOWNLOAD LINK: Deep Learning, Reinforcement Learning, and Neural Networks

Deep_Learning_Reinforcement_Learning_and_Neural_Networks.part1.rar – 1000.0 MB
Deep_Learning_Reinforcement_Learning_and_Neural_Networks.part2.rar – 833.3 MB

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