Course Overview:
Deep Learning refers to the ability of an “artificial agent” to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human’s ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.
Course Objectives:
- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
- Apply advanced Reinforcement Learning algorithms to solve real-world problems
- Build a Deep Learning Agent
Pre-requisites:
- Proficiency in Python
- An understanding of college Calculus and Linear Algebra
- Basic understanding of Probability and Statistics
- Experience creating machine learning models in Python and Numpy
Target Audience:
- Network Administrators, Network (Systems) Engineers, Network (Service) Technicians, Network Analysts, Network Managers
- Junior Programmers, Test Engineers, Test Automation Engineers, QA Engineers and Analysts
Course Duration:
- 3 Days (21 Hours)
Course Content:
- Introduction
- Reinforcement Learning Basics
- Basic Reinforcement Learning Techniques
- Introduction to BURLAP
- The convergence of Value and Policy Iteration
- Reward Shaping
- Exploration
- Generalization
- Partially Observable MDPs
- Options
- Logistics
- TD Lambda
- Policy Gradients
- Deep Q-Learning
- Topics in Game Theory
- Q and A
