Deep Reinforcement Learning: Hands-on AI Tutorial in Python
What you'll learn
- The concepts and fundamentals of reinforcement learning
- The main algorithms including Q-Learning, SARSA as well as Deep Q-Learning.
- How to formulate a problem in the context of reinforcement learning and MDP.
- Apply the learned techniques to some hands-on experiments and real world projects.
- Develop artificial intelligence applications using reinforcement learning.
- Students are assumed to be familiar with python and have some basic knowledge of statistics, and deep learning.
In this course we learn the concepts and fundamentals of reinforcement learning, it's relation to artificial intelligence and machine learning, and how we can formulate a problem in the context of reinforcement learning and Markov Decision Process. We cover different fundamental algorithms including Q-Learning, SARSA as well as Deep Q-Learning. We present the whole implementation of two projects from scratch with Q-learning and Deep Q-Network.
Who this course is for:
- Machine learning and AI enthusiasts and practitioners, data scientists, machine learning engineers.
I am a senior machine learning engineer. I received my Ph.D. degree in computer science from Western Michigan University.
With a background in the Internet of Things and Machine Learning, I am always passionate to combine the power of machine learning to the Internet of Things to make a positive impact on our lives and communities. I’ve contributed to this area in my research works and followed my professional career in the same direction. During my doctoral studies, I focused on the development of advanced machine learning techniques for the Internet of Things. My survey and tutorial research works on IoT and Deep Learning has attracted the attention of researchers and students worldwide. I am the recipient of the best survey paper award 2018 for "Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications", IEEE Communications Surveys & Tutorials journal. I’ve run several workshops on topics like Developing IoT applications, Deep learning, and Reinforcement learning.
I have more than 10 years of software engineering experience. During these years I have worked and gained experience with a wide range of technologies including web development, .Net framework, big data platforms (Apache Hadoop, Spark), cloud computing, and hybrid mobile app development.