Advanced AI Deep Reinforcement Learning in Python
  • ondemand_video
       Video Length : 8h50m0s
  • format_list_bulleted
       Tasks Number : 60
  • group
       Students Enrolled : 1020
  • equalizer
       Medium Level
  • Curriculum
  • 1. Introduction and Logistics
    • videocam
      Introduction and Outline
      10m0s
    • videocam
      Where to get the Code
      10m0s
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      How to Succeed in this Course
      10m0s
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      Tensorflow or Theano - Your Choice!
      10m0s
  • 2. Background Review
    • videocam
      Review Intro
      10m0s
    • videocam
      Review of Markov Decision Processes
      10m0s
    • videocam
      Review of Dynamic Programming
      10m0s
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      Review of Monte Carlo Methods
      10m0s
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      Review of Temporal Difference Learning
      10m0s
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      Review of Approximation Methods for Reinforcement Learning
      10m0s
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      Review of Deep Learning
      10m0s
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques
    • videocam
      OpenAI Gym Tutorial
      10m0s
    • videocam
      Random Search
      10m0s
    • videocam
      Saving a Video
      10m0s
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      CartPole with Bins (Theory)
      10m0s
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      CartPole with Bins (Code)
      10m0s
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      RBF Neural Networks
      10m0s
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      RBF Networks with Mountain Car (Code)
      10m0s
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      RBF Networks with CartPole (Theory)
      10m0s
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      RBF Networks with CartPole (Code)
      10m0s
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      Theano Warmup
      10m0s
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      Tensorflow Warmup
      10m0s
    • videocam
      Plugging in a Neural Network
      10m0s
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      OpenAI Gym Section Summary
      10m0s
  • 4. TD Lambda
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      N-Step Methods
      10m0s
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      N-Step in Code
      10m0s
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      TD Lambda
      10m0s
    • videocam
      TD Lambda in Code
      10m0s
    • videocam
      TD Lambda Summary
      10m0s
  • 5. Policy Gradients
    • videocam
      Policy Gradient Methods
      10m0s
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      Policy Gradient in TensorFlow for CartPole
      10m0s
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      Policy Gradient in Theano for CartPole
      10m0s
    • videocam
      Continuous Action Spaces
      10m0s
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      Mountain Car Continuous Specifics
      10m0s
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      Mountain Car Continuous Theano
      10m0s
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      Mountain Car Continuous Tensorflow
      10m0s
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      Mountain Car Continuous Tensorflow (v2)
      10m0s
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      Mountain Car Continuous Theano (v2)
      10m0s
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      Policy Gradient Section Summary
      10m0s
  • 6. Deep Q-Learning
    • videocam
      Deep Q-Learning Intro
      10m0s
    • videocam
      Deep Q-Learning Techniques
      10m0s
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      Deep Q-Learning in Tensorflow for CartPole
      10m0s
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      Deep Q-Learning in Theano for CartPole
      10m0s
    • videocam
      Additional Implementation Details for Atari
      10m0s
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      Deep Q-Learning in Tensorflow for Breakout
      10m0s
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      Deep Q-Learning in Theano for Breakout
      10m0s
    • videocam
      Partially Observable MDPs
      10m0s
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      Deep Q-Learning Section Summary
      10m0s
    • videocam
      Course Summary
      10m0s
  • 7. Theano and Tensorflow Basics Review
    • videocam
      Theano Basics
      10m0s
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      Theano Neural Network in Code
      10m0s
    • videocam
      Tensorflow Basics
      10m0s
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      Tensorflow Neural Network in Code
      10m0s
Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

Advanced AI Deep Reinforcement Learning in Python


Advanced AI Deep Reinforcement Learning in Python

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