Python Reinforcement Learning, Deep Q-Learning and TRFL

Description

Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from data centre energy saving (cooling data centres) to smart warehousing solutions.

This course covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You will be introduced to the concept of Reinforcement Learning, its advantages and why it's gaining so much popularity. This course also discusses on Markov Decision Process (MDPs), Monte Carlo tree searches, dynamic programmings such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will learn to build convolutional neural network models using TensorFlow and Keras. You will also learn the use of artificial intelligence in a gaming environment with the help of OpenAI Gym.

By the end of this course, you will explore reinforcement learning and will have hands-on experience with real data and artificial intelligence (AI) to build intelligent systems.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

● Lauren Washington is currently the Lead Data Scientist and Machine Learning Developer for smartQED, an AI driven start-up. Lauren worked as a Data Scientist for Topix, Payments Risk Strategist for Google (Google Wallet/Android Pay), Statistical Analyst for Nielsen, and Big Data Intern for the National Opinion Research Center through the University of Chicago. Lauren is also passionate about teaching Machine Learning. She’s currently giving back to the data science community as a Thinkful Data Science Bootcamp Mentor  and a Packt Publishing technical video reviewer. She also earned a Data Science certificate from General Assembly San Francisco (2016), a MA in the Quantitative Methods in the Social Sciences (Applied Statistical Methods) from Columbia University (2012), and a BA in Economics from Spelman College (2010). Lauren is a leader in AI, in Silicon Valley, with a passion for knowledge gathering and sharing.

● Kaiser Hamid Rabbi is a Data Scientist who is super-passionate about Artificial Intelligence, Machine Learning, and Data Science. He has entirely devoted himself to learning more about Big Data Science technologies such as Python, Machine Learning, Deep Learning, Artificial Intelligence, Reinforcement Learning, Data Mining, Data Analysis, Recommender Systems and so on over the last 4 years. Kaiser also has a huge interest in Lygometry (things we know we do not know!) and always tries to understand domain knowledge based on his project experience as much as possible.

● Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and Cloud computing. Over the past few years, they have worked with some of the World's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to better make sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.

● Jim DiLorenzo is a freelance programmer and Reinforcement Learning enthusiast. He graduated from Columbia University and is working on his Masters in Computer Science. He has used TRFL in his own RL experiments and when implementing scientific papers into code.

What You Will Learn!

• Implement state-of-the-art Reinforcement Learning algorithms from the basics
• Discover various techniques of Reinforcement Learning such as MDP, Q Learning, and more
• Dive into Temporal Difference Learning, an algorithm that combines Monte Carlo methods and dynamic programming
• Create a virtual Self Driving Car application with Deep Q-Learning
• Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym
• Build projects with TRFL and TensorFlow and integrate essential RL building blocks into existing code
• Discover improvements to RL algorithms such as DQN and DDPG with TRFL blocks—for example, advanced target network updating, Double Q Learning, and Distributional Q Learning
• Modify RL agents to include multistep reward techniques such as TD lambda
• Create TRFL-based RL agents with classic RL methods such as TD Learning, Q Learning, and SARSA

Who Should Attend!

• This course is designed for AI engineers, Machine Learning engineers, aspiring Reinforcement Learning and Data Science professionals keen to extend their skill set to Reinforcement Learning using Python.

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