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TORRENT DETAILS
[FreeCoursesOnline.Me] [Packt] Hands-On Deep Q-Learning [FCO]
TORRENT SUMMARY
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By combining the power of Reinforcement Learning, Deep Learning, and Machine Learning you will be able to create powerful real-world apps
Video Details
ISBN 9781789957549
Course Length 1 hours 53 minutes
Table of Contents
• GETTING STARTED WITH REINFORCEMENT LEARNING
• FUNDAMENTALS OF DEEP Q-LEARNING
• BUILD SELF DRIVING CAR WITH DEEP Q-LEARNING
• DEEP CONVOLUTIONAL Q-LEARNING INTUITION
• CREATE AN AI WITH DEEP CONVOLUTIONAL Q-LEARNING
Video Description
Do you want to build a virtual self-driving car AI application using the most cutting-edge algorithm of Reinforcement Learning: Deep Q-Learning? Do you want to create an intelligence that can win the famous 90's game—DOOM—by using Deep Convolutional Q-Learning? Deep Q-Learning is the most robust and powerful technique in Artificial Intelligence for solving complex real-world problems. Artificial Intelligence is making our lives easy day by day and reducing human effort everywhere in social media, websites, online stores, and even business. With a less talk and more action approach, this course will lead you through various implementations of Reinforcement Learning techniques by building a virtual self-driving car application and an AI to beat the monsters in DOOM.
You may be wondering that why we create artificial intelligence in a game environment. That is because, once we have created our artificial intelligence in a gaming environment with the help of OpenAIGym, we can use those same principles to solve complex real-world problems just by changing and tweaking algorithm parameters. Get your hands on this course to learn the most fascinating technology in the field of Artificial Intelligence and leverage the power of Reinforcement Learning right away!
You can find the code for this course on GitHub: https://github.com/PacktPublishing/-Hands-On-Deep-Q-Learning/settings/collaboration
Style and Approach
This hands-on course covers all the important aspects of Q-Learning, Deep Q-Learning and Deep Convolutional Q-Learning, the various fields of Reinforcement Learning. And we cover all of those topics by coding in PYTORCH, Kivy, and OpenAIGym. Throughout the course, we will build an intelligent self-driving car by applying Deep Q-Learning and we are going to win Doom with the power of Deep Convolutional Q-Learning!
What You Will Learn
• Get grips on various Reinforcement Learning techniques while building Artificial Intelligence using PYTORCH, Kivy and OpenAIGym
• A solid understanding of Deep Q-Learning intuitions and its functioning
• Optimize performance and efficiency by implementing Deep Q-Learning
• Create a virtual Self Driving Car application with Deep Q-Learning
• Make an Intelligence to win the game named DOOM using Deep Convolutional Q-Learning
• Understand the working behind Artificial Intelligence
Authors
Kaiser Hamid Rabbi
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.
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FILE LIST
Filename
Size
01.Getting started with Reinforcement Learning/0101.Artificial Intelligence in a nutshell.mp4
29.8 MB
01.Getting started with Reinforcement Learning/0102.Reinforcement Learning Dynamics.mp4
49.3 MB
01.Getting started with Reinforcement Learning/0103.The Bellman Equation.mp4
25.3 MB
01.Getting started with Reinforcement Learning/0104.Markov Decision Process.mp4
54.7 MB
01.Getting started with Reinforcement Learning/0105.Policy vs Plan and Living Penalty.mp4
52.6 MB
02.Fundamentals of Deep Q-Learning/0201.Q-Learning Intuition.mp4
40.8 MB
02.Fundamentals of Deep Q-Learning/0202.Temporal Difference.mp4
40.5 MB
02.Fundamentals of Deep Q-Learning/0203.Learning Phase of Deep Q-Learning.mp4
42.6 MB
02.Fundamentals of Deep Q-Learning/0204.Acting Phase of Deep Q-Learning.mp4
43.7 MB
02.Fundamentals of Deep Q-Learning/0205.Experience Reply & Action Selection Policies.mp4
29.5 MB
03.Build Self Driving Car with Deep Q-Learning/0301.Installing PYTORCH environment.mp4
69.7 MB
03.Build Self Driving Car with Deep Q-Learning/0302.Self Driving Car – Part 1.mp4
36.1 MB
03.Build Self Driving Car with Deep Q-Learning/0303.Self Driving Car – Part 2.mp4
16 MB
03.Build Self Driving Car with Deep Q-Learning/0304.Self Driving Car – Part 3.mp4
31.7 MB
03.Build Self Driving Car with Deep Q-Learning/0305.Playing with our SDC AI.mp4
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