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Advanced AI: Deep Reinforcement Learning in Python Download Now

Advanced AI: Deep Reinforcement Learning in Python

Advanced AI: Deep Reinforcement Learning in Python

The Full Information to Mastering Synthetic Intelligence utilizing Deep Learning and Neural Networks

What you’ll be taught
  • Construct varied deep studying brokers (together with DQN and A3C)
  • Apply quite a lot of superior reinforcement studying algorithms to any downside
  • Q-Learning with Deep Neural Networks
  • Coverage Gradient Strategies with Neural Networks
  • Reinforcement Learning with RBF Networks
  • Use Convolutional Neural Networks with Deep Q-Learning
  • Know reinforcement studying fundamentals, MDPs, Dynamic Programming, Monte Carlo, TD Learning
  • Faculty-level math is useful
  • Expertise constructing machine studying fashions in Python and Numpy
  • Know methods to construct ANNs and CNNs utilizing Theano or Tensorflow


This course is all concerning the software of deep studying and neural networks to reinforcement studying.

For those who’ve taken my first reinforcement studying class, then you already know that reinforcement studying is on the bleeding fringe of what we will do with AI.

Particularly, the mix of deep studying with reinforcement studying has led to AlphaGo beating a world champion in the technique sport Go, it has led to self-driving automobiles, and it has led to machines that may play video video games at a superhuman degree.

Reinforcement studying has been round for the reason that 70s however none of this has been attainable till now.

The world is altering at a really quick tempo. The state of California is altering their laws in order that self-driving automobile corporations can check their automobiles with no human in the automobile to oversee.

We’ve seen that reinforcement studying is a wholly completely different type of machine studying than supervised and unsupervised studying.

Supervised and unsupervised machine studying algorithms are for analyzing and making predictions about information, whereas reinforcement studying is about coaching an agent to work together with an setting and maximize its reward.

Not like supervised and unsupervised studying algorithms, reinforcement studying brokers have an impetus – they need to attain a objective.

That is such an interesting perspective, it could actually even make supervised / unsupervised machine studying and “data science” appear boring in hindsight. Why practice a neural community to be taught concerning the information in a database, when you’ll be able to practice a neural community to work together with the real-world?

Whereas deep reinforcement studying and AI has a number of potential, it additionally carries with it enormous threat.

Invoice Gates and Elon Musk have made public statements about among the dangers that AI poses to financial stability and even our existence.

As we realized in my first reinforcement studying course, one of many major rules of coaching reinforcement studying brokers is that there are unintended penalties when coaching an AI.

AIs don’t suppose like people, and they also give you novel and non-intuitive options to achieve their targets, usually in ways in which shock area specialists – people who’re the most effective at what they do.

OpenAI is a non-profit based by Elon Musk, Sam Altman (Y Combinator), and others, in order to make sure that AI progresses in a approach that’s helpful, slightly than dangerous.

A part of the motivation behind OpenAI is the existential threat that AI poses to people. They consider that open collaboration is without doubt one of the keys to mitigating that threat.

One of many nice issues about OpenAI is that they’ve a platform known as the OpenAI Gymnasium, which we’ll be making heavy use of in this course.

It permits anybody, wherever in the world, to coach their reinforcement studying brokers in commonplace environments.

On this course, we’ll construct upon what we did in the final course by working with extra advanced environments, particularly, these offered by the OpenAI Gymnasium:

  • CartPole
  • Mountain Automotive
  • Atari video games

To coach efficient studying brokers, we’ll want new methods.

We’ll lengthen our data of temporal distinction studying by wanting on the TD Lambda algorithm, we’ll have a look at a particular sort of neural community known as the RBF community, we’ll have a look at the coverage gradient technique, and we’ll finish the course by Deep Q-Learning (DQN) and A3C (Asynchronous Benefit Actor-Critic).

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

Instructed Conditions:

  • Faculty-level math is useful (calculus, chance)
  • Object-oriented programming
  • Python coding: if/else, loops, lists, dicts, units
  • Numpy coding: matrix and vector operations
  • Linear regression
  • Gradient descent
  • Know methods to construct ANNs and CNNs in Theano or TensorFlow
  • Markov Resolution Proccesses (MDPs)
  • Know methods to implement Dynamic Programming, Monte Carlo, and Temporal Distinction Learning to resolve MDPs

TIPS (for getting by the course):

  • Watch it at 2x.
  • Take handwritten notes. This can drastically enhance your means to retain the data.
  • Write down the equations. For those who don’t, I assure it can simply appear like gibberish.
  • Ask plenty of questions on the dialogue board. The extra the higher!
  • Understand that almost all workout routines will take you days or perhaps weeks to finish.
  • Write code your self, don’t simply sit there and have a look at my code.


  • Try the lecture “What order should I take your courses in?” (obtainable in the Appendix of any of my programs, together with the free Numpy course)
Who this course is for:
  • Professionals and college students with robust technical backgrounds who want to be taught state-of-the-art AI methods

Created by Lazy Programmer Inc.
Final up to date 7/2020
English [Auto-generated], Indonesian [Auto-generated], 5 extra

Dimension: 2.91 GB

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