Artificial Intelligence 2.0: AI, Python, DRL + ChatGPT Prize
Artificial Intelligence 2.0: AI, Python, DRL + ChatGPT Prize, available at $119.99, has an average rating of 4.29, with 66 lectures, based on 1304 reviews, and has 11498 subscribers.
You will learn about Q-Learning Deep Q-Learning Policy Gradient Actor Critic Deep Deterministic Policy Gradient (DDPG) Twin-Delayed DDPG (TD3) The Foundation Techniques of Deep Reinforcement Learning How to implement a state of the art AI model that is over performing the most challenging virtual applications This course is ideal for individuals who are Data Scientists who want to take their AI Skills to the next level or AI experts who want to expand on the field of applications or Engineers who work in technology and automation or Businessmen and companies who want to get ahead of the game or Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence or Anyone passionate about Artificial Intelligence It is particularly useful for Data Scientists who want to take their AI Skills to the next level or AI experts who want to expand on the field of applications or Engineers who work in technology and automation or Businessmen and companies who want to get ahead of the game or Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence or Anyone passionate about Artificial Intelligence.
Enroll now: Artificial Intelligence 2.0: AI, Python, DRL + ChatGPT Prize
Summary
Title: Artificial Intelligence 2.0: AI, Python, DRL + ChatGPT Prize
Price: $119.99
Average Rating: 4.29
Number of Lectures: 66
Number of Published Lectures: 65
Number of Curriculum Items: 66
Number of Published Curriculum Objects: 65
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Q-Learning
- Deep Q-Learning
- Policy Gradient
- Actor Critic
- Deep Deterministic Policy Gradient (DDPG)
- Twin-Delayed DDPG (TD3)
- The Foundation Techniques of Deep Reinforcement Learning
- How to implement a state of the art AI model that is over performing the most challenging virtual applications
Who Should Attend
- Data Scientists who want to take their AI Skills to the next level
- AI experts who want to expand on the field of applications
- Engineers who work in technology and automation
- Businessmen and companies who want to get ahead of the game
- Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence
- Anyone passionate about Artificial Intelligence
Target Audiences
- Data Scientists who want to take their AI Skills to the next level
- AI experts who want to expand on the field of applications
- Engineers who work in technology and automation
- Businessmen and companies who want to get ahead of the game
- Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence
- Anyone passionate about Artificial Intelligence
Welcome to Artificial Intelligence 2.0!
In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG or TD3, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field).
To approach this model the right way, we structured the course in three parts:
-
Part 1: Fundamentals
In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more. -
Part 2: The Twin-Delayed DDPG Theory
We will study in depth the whole theory behind the model. You will clearly see the whole construction and training process of the AI through a series of clear visualization slides. Not only will you learn the theory in details, but also you will shape up a strong intuition of how the AI learns and works. The fundamentals in Part 1, combined to the very detailed theory of Part 2, will make this highly advanced model accessible to you, and you will eventually be one of the very few people who can master this model. -
Part 3: The Twin-Delayed DDPG Implementation
We will implement the model from scratch, step by step, and through interactive sessions, a new feature of this course which will have you practice on many coding exercises while we implement the model. By doing them you will not follow passively the course but very actively, therefore allowing you to effectively improve your skills. And last but not least, we will do the whole implementation on Colaboratory, or Google Colab, which is a totally free and open source AI platform allowing you to code and train some AIs without having any packages to install on your machine. In other words, you can be 100% confident that you press the execute button, the AI will start to train and you will get the videos of the spider and humanoid running in the end.
So are you ready to embrace AI at full power?
Come join us, never stop learning, and enjoy AI!
Course Curriculum
Chapter 1: Part 1 – Fundamentals
Lecture 1: Welcome Challenge!
Lecture 2: Welcome
Lecture 3: Some resources before we start
Lecture 4: EXTRA: Learning Path
Lecture 5: Q-Learning
Lecture 6: Deep Q-Learning
Lecture 7: Policy Gradient
Lecture 8: Actor-Critic
Lecture 9: Taxonomy of AI models
Lecture 10: EXTRA: 5 Advantages of DRL
Lecture 11: EXTRA: RL Algorithms Map
Lecture 12: Get the materials
Chapter 2: Part 2 – Twin Delayed DDPG Theory
Lecture 1: Introduction and Initialization
Lecture 2: The Q-Learning part
Lecture 3: The Policy Learning part
Lecture 4: The whole training process
Chapter 3: Part 3 – Twin Delayed DDPG Implementation
Lecture 1: The whole code folder of the course with all the implementations
Lecture 2: Beginning
Lecture 3: Implementation – Step 1
Lecture 4: Implementation – Step 2
Lecture 5: Implementation – Step 3
Lecture 6: Implementation – Step 4
Lecture 7: Implementation – Step 5
Lecture 8: Implementation – Step 6
Lecture 9: Implementation – Step 7
Lecture 10: Implementation – Step 8
Lecture 11: Implementation – Step 9
Lecture 12: Implementation – Step 10
Lecture 13: Implementation – Step 11
Lecture 14: Implementation – Step 12
Lecture 15: Implementation – Step 13
Lecture 16: Implementation – Step 14
Lecture 17: Implementation – Step 15
Lecture 18: Implementation – Step 16
Lecture 19: Implementation – Step 17
Lecture 20: Implementation – Step 18
Lecture 21: Implementation – Step 19
Lecture 22: Implementation – Step 20
Chapter 4: The Final Demo!
Lecture 1: Demo – Training
Lecture 2: Demo – Inference
Chapter 5: Annex 1 – Artificial Neural Networks
Lecture 1: Plan of Attack
Lecture 2: The Neuron
Lecture 3: The Activation Function
Lecture 4: How do Neural Networks Work?
Lecture 5: How do Neural Networks Learn?
Lecture 6: Gradient Descent
Lecture 7: Stochastic Gradient Descent
Lecture 8: Backpropagation
Chapter 6: Annex 2 – Q-Learning
Lecture 1: Plan of Attack
Lecture 2: What is Reinforcement Learning?
Lecture 3: The Bellman Equation
Lecture 4: The Plan
Lecture 5: Markov Decision Process
Lecture 6: Policy vs Plan
Lecture 7: Living Penalty
Lecture 8: Q-Learning Intuition
Lecture 9: Temporal Difference
Lecture 10: Q-Learning Visualization
Chapter 7: Annex 3 – Deep Q-Learning
Lecture 1: Plan of Attack
Lecture 2: Deep Q-Learning Intuition – Step 1
Lecture 3: Deep Q-Learning Intuition – Step 2
Lecture 4: Experience Replay
Lecture 5: Action Selection Policies
Chapter 8: Congratulations!! Don't forget your Prize 🙂
Lecture 1: Huge Congrats for completing the challenge!
Lecture 2: Bonus: How To UNLOCK Top Salaries (Live Training)
Instructors
-
Hadelin de Ponteves
Passionate AI Instructor -
SuperDataScience Team
Helping Data Scientists Succeed -
Ligency Team
Helping Data Scientists Succeed
Rating Distribution
- 1 stars: 7 votes
- 2 stars: 14 votes
- 3 stars: 124 votes
- 4 stars: 419 votes
- 5 stars: 740 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
You may also like
- Top 10 Video Editing Courses to Learn in November 2024
- Top 10 Music Production Courses to Learn in November 2024
- Top 10 Animation Courses to Learn in November 2024
- Top 10 Digital Illustration Courses to Learn in November 2024
- Top 10 Renewable Energy Courses to Learn in November 2024
- Top 10 Sustainable Living Courses to Learn in November 2024
- Top 10 Ethical AI Courses to Learn in November 2024
- Top 10 Cybersecurity Fundamentals Courses to Learn in November 2024
- Top 10 Smart Home Technology Courses to Learn in November 2024
- Top 10 Holistic Health Courses to Learn in November 2024
- Top 10 Nutrition And Diet Planning Courses to Learn in November 2024
- Top 10 Yoga Instruction Courses to Learn in November 2024
- Top 10 Stress Management Courses to Learn in November 2024
- Top 10 Mindfulness Meditation Courses to Learn in November 2024
- Top 10 Life Coaching Courses to Learn in November 2024
- Top 10 Career Development Courses to Learn in November 2024
- Top 10 Relationship Building Courses to Learn in November 2024
- Top 10 Parenting Skills Courses to Learn in November 2024
- Top 10 Home Improvement Courses to Learn in November 2024
- Top 10 Gardening Courses to Learn in November 2024