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Development Data Science Python

Advanced AI: Deep Reinforcement Learning in Python

The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks
Rating: 4.6 out of 54.6 (3,740 ratings)
32,658 students
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto], Italian [Auto], 
30-Day Money-Back Guarantee

What you'll learn

  • Build various deep learning agents (including DQN and A3C)
  • Apply a variety of advanced reinforcement learning algorithms to any problem
  • Q-Learning with Deep Neural Networks
  • Policy Gradient Methods with Neural Networks
  • Reinforcement Learning with RBF Networks
  • Use Convolutional Neural Networks with Deep Q-Learning

Course content

12 sections • 79 lectures • 10h 36m total length

  • Preview07:23
  • Where to get the Code
    05:01
  • Anyone Can Succeed in this Course
    12:42
  • Tensorflow or Theano - Your Choice!
    04:09

  • Preview06:34
  • Elements of a Reinforcement Learning Problem
    20:18
  • States, Actions, Rewards, Policies
    09:24
  • Markov Decision Processes (MDPs)
    10:07
  • The Return
    04:56
  • Value Functions and the Bellman Equation
    09:53
  • What does it mean to “learn”?
    07:18
  • Solving the Bellman Equation with Reinforcement Learning (pt 1)
    09:49
  • Solving the Bellman Equation with Reinforcement Learning (pt 2)
    12:01
  • Epsilon-Greedy
    06:09
  • Q-Learning
    14:15
  • How to Learn Reinforcement Learning
    05:56
  • Suggestion Box
    03:03

  • OpenAI Gym Tutorial
    05:43
  • Random Search
    05:48
  • Saving a Video
    02:18
  • CartPole with Bins (Theory)
    03:51
  • CartPole with Bins (Code)
    06:25
  • RBF Neural Networks
    10:26
  • RBF Networks with Mountain Car (Code)
    05:28
  • RBF Networks with CartPole (Theory)
    01:54
  • RBF Networks with CartPole (Code)
    03:11
  • Theano Warmup
    03:04
  • Tensorflow Warmup
    02:25
  • Plugging in a Neural Network
    03:39
  • OpenAI Gym Section Summary
    03:28

  • N-Step Methods
    03:14
  • N-Step in Code
    03:40
  • TD Lambda
    07:36
  • TD Lambda in Code
    03:00
  • TD Lambda Summary
    02:21

  • Policy Gradient Methods
    11:38
  • Policy Gradient in TensorFlow for CartPole
    07:19
  • Policy Gradient in Theano for CartPole
    04:14
  • Continuous Action Spaces
    04:16
  • Mountain Car Continuous Specifics
    04:12
  • Mountain Car Continuous Theano
    07:31
  • Mountain Car Continuous Tensorflow
    08:07
  • Mountain Car Continuous Tensorflow (v2)
    06:11
  • Mountain Car Continuous Theano (v2)
    07:31
  • Policy Gradient Section Summary
    01:36

  • Deep Q-Learning Intro
    03:52
  • Deep Q-Learning Techniques
    09:13
  • Deep Q-Learning in Tensorflow for CartPole
    05:09
  • Deep Q-Learning in Theano for CartPole
    04:48
  • Additional Implementation Details for Atari
    05:36
  • Pseudocode and Replay Memory
    06:15
  • Deep Q-Learning in Tensorflow for Breakout
    23:47
  • Deep Q-Learning in Theano for Breakout
    23:54
  • Partially Observable MDPs
    04:52
  • Deep Q-Learning Section Summary
    04:45

  • A3C - Theory and Outline
    16:30
  • A3C - Code pt 1 (Warmup)
    06:28
  • A3C - Code pt 2
    06:27
  • A3C - Code pt 3
    07:35
  • A3C - Code pt 4
    18:02
  • A3C - Section Summary
    02:05
  • Course Summary
    04:57

  • (Review) Theano Basics
    07:47
  • (Review) Theano Neural Network in Code
    09:17
  • (Review) Tensorflow Basics
    07:27
  • (Review) Tensorflow Neural Network in Code
    09:43

  • Windows-Focused Environment Setup 2018
    20:20
  • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
    17:32

  • How to Code by Yourself (part 1)
    15:54
  • How to Code by Yourself (part 2)
    09:23
  • Proof that using Jupyter Notebook is the same as not using it
    12:29
  • Python 2 vs Python 3
    04:38
  • Is Theano Dead?
    10:03

Requirements

  • Know reinforcement learning basics, MDPs, Dynamic Programming, Monte Carlo, TD Learning
  • College-level math is helpful
  • Experience building machine learning models in Python and Numpy
  • Know how to build ANNs and CNNs using Theano or Tensorflow

Description

This course is all about the application of deep learning and neural networks to reinforcement learning.

If you’ve taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI.

Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level.

Reinforcement learning has been around since the 70s but none of this has been possible until now.

The world is changing at a very fast pace. The state of California is changing their regulations so that self-driving car companies can test their cars without a human in the car to supervise.

We’ve seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.

Supervised and unsupervised machine learning algorithms are for analyzing and making predictions about data, whereas reinforcement learning is about training an agent to interact with an environment and maximize its reward.

Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus - they want to reach a goal.

This is such a fascinating perspective, it can even make supervised / unsupervised machine learning and "data science" seem boring in hindsight. Why train a neural network to learn about the data in a database, when you can train a neural network to interact with the real-world?

While deep reinforcement learning and AI has a lot of potential, it also carries with it huge risk.

Bill Gates and Elon Musk have made public statements about some of the risks that AI poses to economic stability and even our existence.

As we learned in my first reinforcement learning course, one of the main principles of training reinforcement learning agents is that there are unintended consequences when training an AI.

AIs don’t think like humans, and so they come up with novel and non-intuitive solutions to reach their goals, often in ways that surprise domain experts - humans who are the best at what they do.

OpenAI is a non-profit founded by Elon Musk, Sam Altman (Y Combinator), and others, in order to ensure that AI progresses in a way that is beneficial, rather than harmful.

Part of the motivation behind OpenAI is the existential risk that AI poses to humans. They believe that open collaboration is one of the keys to mitigating that risk.

One of the great things about OpenAI is that they have a platform called the OpenAI Gym, which we’ll be making heavy use of in this course.

It allows anyone, anywhere in the world, to train their reinforcement learning agents in standard environments.

In this course, we’ll build upon what we did in the last course by working with more complex environments, specifically, those provided by the OpenAI Gym:

  • CartPole

  • Mountain Car

  • Atari games

To train effective learning agents, we’ll need new techniques.

We’ll extend our knowledge of temporal difference learning by looking at the TD Lambda algorithm, we’ll look at a special type of neural network called the RBF network, we’ll look at the policy gradient method, and we’ll end the course by looking at Deep Q-Learning (DQN) and A3C (Asynchronous Advantage Actor-Critic).

Thanks for reading, and I’ll see you in class!


"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • 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?

  • 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:

  • College-level math is helpful (calculus, probability)

  • Object-oriented programming

  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy coding: matrix and vector operations

  • Linear regression

  • Gradient descent

  • Know how to build ANNs and CNNs in Theano or TensorFlow

  • Markov Decision Proccesses (MDPs)

  • Know how to implement Dynamic Programming, Monte Carlo, and Temporal Difference Learning to solve MDPs


WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Who this course is for:

  • Professionals and students with strong technical backgrounds who wish to learn state-of-the-art AI techniques

Featured review

Unnat Antani
Unnat Antani
3 courses
2 reviews
Rating: 5.0 out of 511 months ago
Much better course than other courses on similar subject. The code base is always up to date so the user doesn't have to sit and painstakingly debug the code due to out-dated versions of libraries used.

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Team
  • 4.6 Instructor Rating
  • 40,699 Reviews
  • 148,448 Students
  • 14 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,547 Reviews
  • 423,229 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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