$399 – $525

Principles of Reinforcement Learning [Deep Learning] - 6 Early Bird Tickets...

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Santa Clara

Santa Clara, CA

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No Refunds

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Description

Principles of Reinforcement Learning

Objective

This class is intended for people who want to better understand Reinforcement Learning(RL). RL utilizes efficient trial and error techniques to find optimal solutions. RL techniques have been recently deployed in optimizing supervised network hyperparameters, solving games, economic models and multidimensional real world problems.

Students will be using Tensorflow and Python libraries to better explore this space to solve deep learning problems. We will learn the theory and as well as build programing labs uning interesting gaming environments to better understand the capabilitites, advantages and some best practices. Each student will have his/her own cloud-based GPU instance and will use Jupyter notebook for deep learning model development. A Laptop is requied.

Agenda

(Lunch, beverages and light snacks will be served)

  • AI Landscape and how RL fits into it

  • Basics of Reinforcement Learning

  • Policy Gradient Techniques

  • Leading papers and current research

  • Self-play and Gameplay

Intended Audience

Programmers, analysts, managers, investors, enthusiast pretty much anyone technically curious about deploying Machine Learning.

What do I need to know to be successful:

All you need is curiosity in the subject and access to a laptop for which you have admin access.
We will grant you access to an cloud instance for conducting the hands-on exercise.
About 40% of the class is lectures and 60% is hands on programming.
You will need to bring a computer with chrome browser and internet connectivity.
In order to do the hands-on programming segment you need to know Python.
A general understanding of numpy, scipy and tensorflow would help. Since people have varying experience with this, we will be providing a quick overview.

Learn more @ training.leftbrain.consulting

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Date and Time

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Santa Clara

Santa Clara, CA

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Refund Policy

No Refunds

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