Deep Learning on Azure with Python: Reinforcement Learning
- Offered byFutureLearn
Deep Learning on Azure with Python: Reinforcement Learning at FutureLearn Overview
Duration | 6 weeks |
Total fee | ₹900 |
Mode of learning | Online |
Official Website | Go to Website  |
Credential | Certificate |
Deep Learning on Azure with Python: Reinforcement Learning at FutureLearn Highlights
- Duration 6 weeks
- Weekly study 5 hours
- 100% online Try this course for free
Deep Learning on Azure with Python: Reinforcement Learning at FutureLearn Course details
- This machine learning course focuses on reinforcement learning and how it uses artificial intelligence to find the best possible solution to complex problems involving multiple decisions.
- Reinforcement learning acknowledges the multifaceted, multilevel nature of the problems we use machine learning to solve. These challenges might be viewed as a sequence, with each resolved challenge creating or limiting possibilities to solve the next.
- Framing these challenges as relational learning problems allows us to explore every potential path through a sequence of decisions. This allows artificial intelligence to determine the most effective or efficient solution to complex problems.
- Reinforcement learning can be applied to neural networks used in deep learning, helping us to build more refined algorithms.
- This course will give you an introduction to reinforcement learning using Python, in Microsoft Azure. You'll learn how to frame relational learning problems. You'll get an introduction to common relational learning algorithms, including dynamic programming algorithms and temporal difference learning. And you'll discover Project Malmo - a platform for AI experimentation built in Minecraft.
- By the end of this course, you will have developed a clear understanding of reinforcement learning techniques, and the relevant formal notation. You'll then be able to apply these in Microsoft Azure Cognitive Services, using Python programming.
Deep Learning on Azure with Python: Reinforcement Learning at FutureLearn Curriculum
Course Introduction
About this Course
What is Reinforcement Learning?
Applications of Reinforcement Learning
Comparisons To Machine Learning
Elements of Reinforcement Learning
CloudSwyft Hands-On Lab: RL Environments and Random Agent
Wrapping Up the Week
Introduction to Reinforcement Learning
Bandits Framework
Regret Minimisation
Bridge to Reinforcement Learning
CloudSwyft Hands-On Lab: Bandits
Wrapping Up the Week
The Reinforcement Learning Problem
Agent and Environment Interface
Markov Decision Process
CloudSwyft Hands-On Lab 3
Basics of Dynamic Programming
Wrapping up the week
Applying Dynamic Programming & Policy Evaluation
CloudSwyft Hands-On Lab 4
Temporal Difference Learning - Policy Evaluation
Temporal Difference Learning - Policy Optimisation
CloudSwyft Hands-On Lab 5
Wrapping Up the Week
Function Approximation and Deep Q-Learning
Function Approximation
CloudSwyft Hands-On Lab 6
RL with Deep Neural Networks
Extensions to Deep Q-Learning
CloudSwyft Hands-On Lab 7
Introduction to Policy Optimisation
Wrapping Up the Week
Policy Gradient and Actor Critic
Likelihood Ratio Methods
CloudSwyft Hands-On Lab 8
Variance Reduction
CloudSwyft Hands-On Lab 9
Actor-Critic
CloudSwyft Hands-On Lab 10
Course Completion
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