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9 Nov 2018
Introduction To Artificial Intelligence & It Applications
Hashedin
Anshuman Singh
#IT | 2 min read
Introduction To Artificial Intelligence & It Applications
#IT
Hashedin
Anshuman Singh

HashedIn is thrilled to host School of AI, meetups designed for the thriving community of AI hobbyists. We partner with the most influential and innovative leaders in AI to inspire and educate developers. Our sessions focus on AI basics, applications and use cases, reinforcement learning, neural networks and much more.

Getting Started With Artificial Intelligence

Session 1:

The first session was for beginners, who are just getting started with AI. It started with simple concepts and examples.

Here is a quick outline of what you will find in the Video embedded below.

  • A simple min-max search algorithm to play chess and how IBM Deepblueused basically a min-max search and lots of computing power to defeat world chess champion Kasparov in 1997.
  • IBM Watson and its win in Jeopardy in 2012. And how the seemingly easy natural language understanding is much harder for machines.
  • Google’s AlphaGo and its probabilistic search and deep learning based heuristic that defeated Lee Sedol at a much more complex game (compared to chess) of Go.
  • Reinforcement Learning Machines that get better by playing themselves
  • Some of the AI work done at HashedIn

Session 2:

The session is centered around practical introduction to Reinforcement Learning. Here the basics of OpenAI Gym and Reinforcement Learning from a Deep Learning perspective are covered.

The only prerequisites were:

1. Basics of Linear Algebra
2. Basics of the neural network will be a plus

Here is a quick outline of what you will find in the Video embedded below.

  • Reinforcement Learning vs Traditional Deep Learning
  • The Six Ingredients of Reinforcement Learning
  • Markov Decision Process and Value Iteration
  • A brief overview of the state of the art Approaches
  • A Code Walkthrough of some cool demo

Reinforcement Learning Part 1 

Reinforcement Learning Part 2

Reinforcement Learning Part 3


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