Published 6/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.42 GB | Duration: 4h 33m
Mastering Deep Q-Learning: Unveiling the Code and Math Behind Intelligent Navigation
What you’ll learn
Deep Q-Learning fundamentals
Code implementation of Deep Q-Learning
Mathematical foundations of Deep Q-Learning
Building a navigation agent from scratch
Python programming for reinforcement learning
Understanding state representation
Action selection strategies
Reward computation
Q-value estimation
DQN (Deep Q-Network) architecture
Neural network layers and their role
Exploration-exploitation trade-off
Optimization algorithms
Loss functions and gradients
Backpropagation
Explaining the math behind Deep Q-Learning
Requirements
Basic knowledge of Python programming language
Familiarity with fundamental concepts of reinforcement learning
Understanding of basic mathematical concepts (linear algebra, calculus)
Description
Embark on a captivating journey into the realm of Deep Q-Learning and unravel the secrets behind intelligent navigation. In this immersive course, we delve deep into the code and math that power this groundbreaking reinforcement learning technique. Brace yourself for an exhilarating exploration where you’ll gain a comprehensive understanding of Deep Q-Learning while dissecting each line of code, peering into the intricacies of the mathematical foundations.Throughout this course, you’ll undertake an exciting project that brings Deep Q-Learning to life. By building a powerful agent from scratch, you’ll witness firsthand the transformation of a blank slate into an intelligent navigator. With Python and the PyTorch library as your tools, you’ll embark on a mission to navigate a grid-based environment, with the ultimate goal of reaching a designated target location.As you progress, you’ll unravel the mysteries of the math behind Deep Q-Learning. Every step of the way, we’ll meticulously explain the mathematical concepts underpinning the code, ensuring you develop a solid grasp of the underlying principles. From state representation and action selection to reward computation and Q-value estimation, you’ll gain a deep understanding of the mathematical foundations that drive intelligent decision-making.Guided by expert instructors, you’ll explore the inner workings of the DQN (Deep Q-Network) model, comprehending the architecture and its role in approximating Q-values. You’ll dive into the intricacies of neural networks, witnessing how each layer contributes to the agent’s decision-making process. By dissecting the code and examining the model’s behavior, you’ll uncover the secrets behind intelligent action selection.But that’s not all – you’ll also tackle the challenges of training the agent. Discover the exploration-exploitation trade-off as you learn to balance the agent’s curiosity and exploitation of learned knowledge. Witness the power of optimization algorithms and delve into the intricacies of loss functions, gradients, and backpropagation. Through rigorous training, you’ll witness the agent’s continuous improvement, learning how to mold its behavior through the application of rewards and penalties.By the end of this course, you’ll emerge as a proficient Deep Q-Learning practitioner, equipped with the knowledge and skills to design intelligent agents capable of navigating complex environments. You’ll have a deep understanding of the fundamental concepts, the ability to dissect and comprehend code, and the expertise to explain the math behind each line. Prepare to unlock the potential of Deep Q-Learning and embark on a transformative learning journey like no other.Enroll now and unravel the power of Deep Q-Learning with code and math as your guides!
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Course Content
Lecture 2 1 A quick look at the first project
Lecture 3 2 Understand fully connected linear layer
Lecture 4 3 Understand Forward method
Lecture 5 4 Understand the Math of ADAM OPTIMIZER
Lecture 6 5 Understand How to take actions
Lecture 7 6 Undertstan Q-Learning Paramaters
Lecture 8 7 Finding Q-value and Next Q-Value
Lecture 9 8 Understand How to Combine Q-Learning and Deep Learning
Lecture 10 9 Learn how to optimize paramaters
Lecture 11 10 Understand the Training loop
Lecture 12 11 Create Q-Table
Lecture 13 12 Print the Optimal Path According to Q-Table
Lecture 14 13 Add one obstacle to the environment
Lecture 15 14 Optimize the code to solve 3×3 envronment
Lecture 16 15 Save and Load the Model
Students and learners interested in reinforcement learning and its applications,Data scientists and machine learning practitioners wanting to expand their knowledge in Deep Q-Learning,Programmers and developers looking to implement intelligent navigation systems,Researchers and academics exploring the field of artificial intelligence and deep learning,Professionals seeking to enhance their understanding of Q-Learning and its mathematical foundations,Enthusiasts interested in building intelligent agents and exploring the intersection of code and math
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