LIVE TRAINING: July 20th
12 PM EST
Price: $187
Regular price $210, discounted 10%
4 hour immersive session
Hands-on training with Q&A
Recording available on-demand
Certification of Completion
10% Discount Ends in:
Subscribe and get an additional 10% to 35% off ALL live training session
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
– TensorFlow
– 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
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
DATE
TIME:
DURATION:
LEVEL:
JULY 20TH, 2021
TIME: 12 PM EST, 9 AM PST
4 HOURS
INTERMEDIATE – ADVANCED
Prerequisites
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
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