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Price: $187

Regular price $210, discounted 10%

  • 4 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

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Subscribe and get an additional 10% to 35% off ALL live training session

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Meet Your Instructor

Amita Kapoor

Amita Kapoor, is the author of best-selling books in the field of Artificial Intelligence and Deep Learning. She mentors students at different online platforms such as Udacity and Coursera and is a research and tech advisor to organizations like DeepSight AI Labs and MarkTechPost. She started her academic career in the Department of Electronics, SRCASW, the University of Delhi, where she is an Associate Professor. She has over 20 years of experience in actively researching and teaching neural networks and artificial intelligence at the university level. A DAAD fellow, she has won many accolades with the most recent being Intel AI Spotlight award 2019, Europe. An active researcher, she has more than 50 publications in international journals and conferences. Extremely passionate about using AI for the betterment of society and humanity in general, she is ready to embark on her second innings as a digital nomad.

Course Overview

In recent years, there has been increased interest in the field of deep reinforcement learning (DRL), fuelled mainly by its performance in Atari Games and the win of AlphaGo over Mr. Lee Sedol, a Dan 9 Go player. Unlike supervised learning, reinforcement learning does not require labeled data. Here the agent learns through its interaction with the environment. DRL combines deep learning for sensory processing along with reinforcement learning algorithms. In this interactive training, you will be introduced to some of the most popular and successful DRL algorithms. We will start with an introduction to different learning paradigms and how DRL differs from them. We will introduce the OpenAI reinforcement learning environment and learn how to use the OpenAI Gym to design your (custom) environments. The two major RL methods: value-based methods and policy-based methods will be explored. We will cover the Deep Q Network and use it to solve a discrete action space environment. Policy gradient methods will also be explored with a special emphasis on continuous action space and multi-agent environment. Finally, we will cover the pros and cons of different algorithms and proposed variations in them. The training session will include a hands-on session where you will build an RL agent for playing Atari. Additionally, we will cover how to use RL agents in other applications like robotics, the financial sector, etc.

Why Enroll?

By the end of the course, participants will be able to:

  • Gain knowledge of the latest algorithms used in reinforcement learning.

  • Understand OpenAI Gym environment

  • Build your custom environment in Gym

  • Using TensorFlow build an RL agent to play the Game of Atari

  • Learn to apply RL in tasks other than games


Course Outline

Module 1: Introduction to RL – Theory

What is Reinforcement Learning

RL vs Supervised Learning and Unsupervised Learning

RL Components – states, actions, rewards, policy, and value functions

RL Formalisations – Multi-armed Bandits, MDP, POMDP, Bellman Equation

RL Environments – Google Dopamine, Unity ml-agents, OpenAI Gym

Module 2: Open AI Gym and TensorFlow 101 – Practical Hands-On

Open AI Gym


Q Table-based Implementation

Building Custom Environments in Gym

Module 3: DRL Algorithm Implementations

Deep Q Network

Policy Gradients

Deep Deterministic Policy Gradient networks

– Applications of RL in finance

-Application of RL in robotics

– Road Ahead

Real-world use-cases
  • Reinforcement Learning has shown great possibilities. The winning of AlphaGo Zero an RL agent developed by Deep Mind in the game of Go created ripples in the Deep Learning world. Today reinforcement learning is used in many industrial applications. Self-driving cars, industry automation, trading, and finance are some of the use cases where RL algorithms have been successfully employed.

Key Details





JULY 20TH, 2021





The training is targeted at research students and machine learning/deep learning engineers with experience in supervised learning.

The audience should be aware of the basic deep learning algorithms, specifically Convolutional Neural Networks and Stochastic Gradient.

Basic knowledge of Python language and one of the deep learning frameworks such as PyTorch or TensorFlow will be useful.

Upcoming Live Training

August 3rd

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This tutorial will provide a hands-on guide on how to approach a network analysis project from scratch and end-to-end: how to generate, manipulate, analyze and visualize graph structures that will help you gain insight about relationships between elements in your data. You will learn how to detect communities in network to identify more densely interconnected subgroups used on social media platforms to detect social groups, and how to most effectively highlight them in a graph visualization.

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Open Data Science

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