Cutting-Edge AI: Deep Reinforcement Learning in Python
Cutting-Edge AI: Deep Reinforcement Learning in Python, available at $99.99, has an average rating of 4.64, with 51 lectures, based on 2846 reviews, and has 34687 subscribers.
You will learn about Understand a cutting-edge implementation of the A2C algorithm (OpenAI Baselines) Understand and implement Evolution Strategies (ES) for AI Understand and implement DDPG (Deep Deterministic Policy Gradient) Understand important foundations for OpenAI ChatGPT, GPT-4 This course is ideal for individuals who are Students and professionals who want to apply Reinforcement Learning to their work and projects or Anyone who wants to learn cutting-edge Artificial Intelligence and Reinforcement Learning algorithms It is particularly useful for Students and professionals who want to apply Reinforcement Learning to their work and projects or Anyone who wants to learn cutting-edge Artificial Intelligence and Reinforcement Learning algorithms.
Enroll now: Cutting-Edge AI: Deep Reinforcement Learning in Python
Summary
Title: Cutting-Edge AI: Deep Reinforcement Learning in Python
Price: $99.99
Average Rating: 4.64
Number of Lectures: 51
Number of Published Lectures: 51
Number of Curriculum Items: 51
Number of Published Curriculum Objects: 51
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Understand a cutting-edge implementation of the A2C algorithm (OpenAI Baselines)
- Understand and implement Evolution Strategies (ES) for AI
- Understand and implement DDPG (Deep Deterministic Policy Gradient)
- Understand important foundations for OpenAI ChatGPT, GPT-4
Who Should Attend
- Students and professionals who want to apply Reinforcement Learning to their work and projects
- Anyone who wants to learn cutting-edge Artificial Intelligence and Reinforcement Learning algorithms
Target Audiences
- Students and professionals who want to apply Reinforcement Learning to their work and projects
- Anyone who wants to learn cutting-edge Artificial Intelligence and Reinforcement Learning algorithms
Ever wondered how AI technologies like OpenAI ChatGPT and GPT-4 really work? In this course, you will learn the foundations of these groundbreaking applications.
Welcome to Cutting-Edge AI!
This is technically Deep Learning in Python part 11of my deep learning series, and my 3rd reinforcement learning course.
Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks).
While both of these have been around for quite some time, it’s only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning.
The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer.
Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be.
We’ve seen how AlphaZero can master the game of Go using only self-play.
This is just a few years after the original AlphaGo already beat a world champion in Go.
We’ve seen real-world robots learn how to walk, and even recover after being kicked over, despite only being trained using simulation.
Simulation is nice because it doesn’t require actual hardware, which is expensive. If your agent falls down, no real damage is done.
We’ve seen real-world robots learn hand dexterity, which is no small feat.
Walking is one thing, but that involves coarse movements. Hand dexterity is complex – you have many degrees of freedom and many of the forces involved are extremely subtle.
Imagine using your foot to do something you usually do with your hand, and you immediately understand why this would be difficult.
Last but not least – video games.
Even just considering the past few months, we’ve seen some amazing developments. AIs are now beating professional players in CS:GO and Dota 2.
So what makes this course different from the first two?
Now that we know deep learning works with reinforcement learning, the question becomes: how do we improve these algorithms?
This course is going to show you a few different ways: including the powerful A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolution strategies.
Evolution strategies is a new and fresh take on reinforcement learning, that kind of throws away all the old theory in favor of a more “black box” approach, inspired by biological evolution.
What’s also great about this new course is the variety of environments we get to look at.
First, we’re going to look at the classic Atari environments. These are important because they show that reinforcement learning agents can learn based on images alone.
Second, we’re going to look at MuJoCo, which is a physics simulator. This is the first step to building a robot that can navigate the real-world and understand physics – we first have to show it can work with simulated physics.
Finally, we’re going to look at Flappy Bird, everyone’s favorite mobile game just a few years ago.
Thanks for reading, and I’ll see you in class!
“If you can’t implement it, you don’t understand it”
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Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
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My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
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Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
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After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…
Suggested prerequisites:
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Calculus
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Probability
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Object-oriented programming
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Python coding: if/else, loops, lists, dicts, sets
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Numpy coding: matrix and vector operations
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Linear regression
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Gradient descent
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Know how to build a convolutional neural network (CNN) in TensorFlow
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Markov Decision Proccesses (MDPs)
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
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Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)
UNIQUE FEATURES
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Every line of code explained in detail – email me any time if you disagree
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No wasted time “typing” on the keyboard like other courses – let’s be honest, nobody can really write code worth learning about in just 20 minutes from scratch
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Not afraid of university-level math – get important details about algorithms that other courses leave out
Course Curriculum
Chapter 1: Welcome
Lecture 1: Introduction
Lecture 2: Outline
Lecture 3: Where to get the code
Chapter 2: Review of Fundamental Reinforcement Learning Concepts
Lecture 1: Review Section Introduction
Lecture 2: The Explore-Exploit Dilemma
Lecture 3: Markov Decision Processes (MDPs)
Lecture 4: Monte Carlo Methods
Lecture 5: Temporal Difference Learning (TD)
Lecture 6: OpenAI Gym Warmup
Lecture 7: Review Section Summary
Lecture 8: Suggestion Box
Chapter 3: A2C (Advantage Actor-Critic)
Lecture 1: A2C Section Introduction
Lecture 2: A2C Theory (part 1)
Lecture 3: A2C Theory (part 2)
Lecture 4: A2C Theory (part 3)
Lecture 5: A2C Demo
Lecture 6: A2C Code – Rough Sketch
Lecture 7: Multiple Processes
Lecture 8: Environment Wrappers
Lecture 9: Convolutional Neural Network
Lecture 10: A2C
Lecture 11: A2C Section Summary
Chapter 4: DDPG (Deep Deterministic Policy Gradient)
Lecture 1: DDPG Section Introduction
Lecture 2: Deep Q-Learning (DQN) Review
Lecture 3: DDPG Theory
Lecture 4: MuJoCo
Lecture 5: DDPG Code (part 1)
Lecture 6: DDPG Code (part 2)
Lecture 7: DDPG Section Summary
Chapter 5: ES (Evolution Strategies)
Lecture 1: ES Section Introduction
Lecture 2: ES Theory
Lecture 3: Notes on Evolution Strategies
Lecture 4: ES for Optimizing a Function
Lecture 5: ES for Supervised Learning
Lecture 6: Flappy Bird
Lecture 7: ES for Flappy Bird in Code
Lecture 8: ES for MuJoCo in Code
Lecture 9: ES Section Summary
Chapter 6: Setting Up Your Environment (FAQ by Student Request)
Lecture 1: Pre-Installation Check
Lecture 2: Anaconda Environment Setup
Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Chapter 7: Extra Help With Python Coding for Beginners (FAQ by Student Request)
Lecture 1: How to Code by Yourself (part 1)
Lecture 2: How to Code by Yourself (part 2)
Lecture 3: Proof that using Jupyter Notebook is the same as not using it
Lecture 4: Python 2 vs Python 3
Chapter 8: Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Lecture 1: How to Succeed in this Course (Long Version)
Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
Lecture 3: Machine Learning and AI Prerequisite Roadmap (pt 1)
Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 2)
Chapter 9: Appendix / FAQ Finale
Lecture 1: What is the Appendix?
Lecture 2: BONUS
Instructors
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Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Rating Distribution
- 1 stars: 14 votes
- 2 stars: 19 votes
- 3 stars: 83 votes
- 4 stars: 1089 votes
- 5 stars: 1641 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?
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