Deep Reinforcement Learning
-
COURSE DATES AND LOCATIONS
DATE
Duration
LOCATION
FEES
Book Now
-
INTRODUCTION
This course introduces Deep Reinforcement Learning (RL), one of the latest machine learning technologies. Deep RL has attracted the attention of many researchers and developers in recent years due to its wide range of applications in a variety of fields such as robotics, robotic surgery, pattern recognition, medical image-based diagnosis, treatment strategies in clinical decision-making, personalized medical treatment and drug discovery and recognition. on speech, computer vision, and natural language processing. Deep reinforcement learning combines artificial neural networks with a framework of reinforcement learning that helps software agents learn how to reach their goals.
-
COURSE OBJECTIVES
By the end of the course, you will be able to:
- Learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field).
- we structured the course in three parts:
- Part 1: Fundamentals
- Part 2: The Twin-Delayed DDPG Theory
- Part 3: The Twin-Delayed DDPG Implementation
-
COURSE AUDIENCE
This course is made for :
- Businessmen and companies who want to get ahead of the game
- AI experts who want to expand on the field of applications
- Engineers who work in technology and automation
- Data Scientists who want to take their AI Skills to the next level
- Anyone passionate about Artificial Intelligence
- Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence
-
COURSE OUTLINE
Day One
- Q-Learning
- Deep Deterministic Policy Gradient (DDPG)
- Understand the main fundamentals that drive Deep Learning
Day Two
- Policy Gradient
- Actor Critic
- Be able to build, train and apply fully connected deep neural networks
Day Three
- Twin-Delayed DDPG (TD3)
- The Foundation Techniques of Deep Reinforcement Learning
- Know how to implement efficient CNN or RNN
Day Four
- How to Implement a State of the Art AI Model That is Over Performing the Most Challenging Virtual Application
Day Five
- Deep Q-Learning
- Understand the key features in a neural network’s architecture
