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https://www.eventshigh.com/detail/mumbai/903f8393396c263b360aad6d56899b4f-artificial-intelligence-a-two-day

Artificial Intelligence: A Two Day Course on Deep Reinforcement Learning.

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Sat, 1 Jun 11:00AM - Sun, 2 Jun 6:00PM
riidl
Rs 3000
475 people viewed this event.

Pricing & Offers

Pricing & Offers
Zero convenience fee, ticket price shown below is the final price.
Sat, 1 Jun
, 11:00AM
- Sun, 2 Jun
, 6:00PM
Per Person
₹ 3,000
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Details

Details

Reinforcement Learning(RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.

Reinforcement Learning is the best AI technique for Complex Industrial Systems. The widely published successes of DeepMinds AlphaZero and OpenAIs Dota bot have opened even more eyes to the almost magical capabilities of deep reinforcement learning agents.



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SPEAKER

SPEAKER

Vikramank Singh

UC Berkeley | Ex-Facebook | TEDx

 

Data Scientist with a strong background in Mathematics and Computer Science and experience in Data Analytics, Visualization, Data mining, Statistics and Machine Learning. Passionate about telling stories behind data to non-technical audiences. Frequent public speaker about Machine Learning, Deep Learning, Data Analytics and Artificial Intelligence at various National and International conferences.

 

Former Research Fellow at Indian Academy of Sciences, Bangalore and Indian Institute of Management, Ahmedabad. Responsible for driving product research at companies big or small. Worked with Red Hat, Inc, Adapty, Inc to develop their analytics engines/pipelines for automated reports and statistics. Led research at Institute of Nuclear Medicine and Allied Sciences - DRDO, Ministry of Defence, Govt. of India, Cognitive Science Lab. Worked at Facebook as a lead Research Engineer in the Connectivity Lab team to build the Offline Social Network.

 

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COURSE CONTENT

COURSE CONTENT

 COURSE CONTENT

  1. Introduction to Reinforcement Learning
  2. Markov Decision Processes
  3. Supervised Learning and Imitation Learning
  4. Model-Free Reinforcement Learning
  5. Q-learning, Policy Gradients, Actor-Critic, etc.
  6. Model-Based Reinforcement Learning (Monte Carlo Tree Search, etc.)
  7. Topic to study in the future (Inverse Reinforcement Learning, Meta-Learning, etc)
  8. Case Study/Project

 

PREREQUISITE

Python Programming.

 

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Map & Directions

Map & Directions
riidl Room No. 520, Bhaskarachraya Building, Somaiya Vidyavihar, Vidyanagar, Vidya Vihar East, Vidyavihar, Mumbai, Maharashtra 400077, India
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