Applied Deep Learning: Generative Adversarial Networks and Q-Learning

Machine Learning
  • 11 Videos | 49m 25s
  • Includes Assessment
  • Earns a Badge
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Learners will explore variations of generative adversarial network (GAN) and the challenges associated with its models, as well as the concept of deep reinforcement learning, its application for machine learning, and how it differs from deep learning, in this 11-video course. Begin by implementing autoencoders with Keras and Python; implement GAN and the role of Generator and Discriminator; and implement GAN Discriminator and Generator with Python and Keras and build Discriminator for training models. Discover the challenges of working with GAN models and explore the concept of deep reinforcement learning and its application in the areas of robotics, finance, and health care. Compare deep reinforcement learning with deep learning, and examine challenges associated with their implementations. Learn about the basic concepts of reinforcement learning, as well as the concept of deep Q-learning and implementing deep Q-learning. Then implement deep Q-learning in Python by using Keras and OpenAI Gym. The concluding exercise involves recalling variations of GAN, implementing GAN Discriminator and Generator using Python, and implementing deep Q-learning in Python by using Keras and OpenAI Gym.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    use deep convolutional autoencoder with Keras and Python
    implement generative adversarial network and the role of Generator and Discriminator
    implement generative adversarial network Discriminator and Generator using Python and Keras and build Discriminator for training model
    recognize the challenges of working with generative adversarial network models
  • describe the concept of deep reinforcement learning and its application in the areas of robotics, finance, and healthcare
    compare deep reinforcement learning with deep learning, and describe the challenges associated with their implementations
    describe the basic concepts of reinforcement learning, as well as the concept of deep Q-learning and its implementation
    implement deep Q-learning in Python using Keras and OpenAI Gym
    recall the variations of generative adversarial network, implement generative adversarial network Discriminator and Generator using Python, and implement deep Q-learning in Python using Keras and OpenAI Gym

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 1s
    UP NEXT
  • Playable
    2. 
    Implement Autoencoder Using Keras
    6m 43s
  • Locked
    3. 
    Implementing Generative Adversarial Networks
    4m 48s
  • Locked
    4. 
    Building GAN Model Using Python and Keras
    6m 4s
  • Locked
    5. 
    Generative Adversarial Network Challenges
    2m 52s
  • Locked
    6. 
    Deep Reinforcement Learning
    4m 48s
  • Locked
    7. 
    Deep RL and Deep Learning Comparison
    4m 14s
  • Locked
    8. 
    Generative Adversarial Network Variations
    3m 25s
  • Locked
    9. 
    Deep Q-Learning
    3m 45s
  • Locked
    10. 
    Deep Q-Learning in Python
    3m 35s
  • Locked
    11. 
    Exercise: Implementing GAN and Deep Q-Learning
    3m 40s

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