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!! Dont forget your Prize 馃檪

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!