Applied Deep Learning: Generative Adversarial Networks and Q-Learning

Machine Learning    |    Intermediate
  • 11 videos | 44m 55s
  • 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
  • Playable
    2.  Implement Autoencoder Using Keras
    6m 43s
    Learn how to use a deep convolutional autoencoder with Keras and Python. FREE ACCESS
  • Locked
    3.  Implementing Generative Adversarial Networks
    4m 48s
    In this video, find out how to implement a generative adversarial network and the role of the Generator and Discriminator. FREE ACCESS
  • Locked
    4.  Building GAN Model Using Python and Keras
    6m 4s
    In this video, find out how to implement a generative adversarial network Discriminator and Generator using Python and Keras and build a Discriminator for training the model. FREE ACCESS
  • Locked
    5.  Generative Adversarial Network Challenges
    2m 52s
    Upon completion of this video, you will be able to recognize the challenges of working with generative adversarial network models. FREE ACCESS
  • Locked
    6.  Deep Reinforcement Learning
    4m 48s
    Upon completion of this video, you will be able to describe the concept of deep reinforcement learning and its applications in the areas of robotics, finance, and healthcare. FREE ACCESS
  • Locked
    7.  Deep RL and Deep Learning Comparison
    4m 14s
    In this video, you will learn how to compare deep reinforcement learning with deep learning, and describe the challenges associated with their implementations. FREE ACCESS
  • Locked
    8.  Generative Adversarial Network Variations
    3m 25s
  • Locked
    9.  Deep Q-Learning
    3m 45s
    Upon completion of this video, you will be able to describe the basic concepts of reinforcement learning, as well as the concept of deep Q-learning and its implementation. FREE ACCESS
  • Locked
    10.  Deep Q-Learning in Python
    3m 35s
    During this video, you will learn how to implement deep Q-learning in Python using the Keras and OpenAI Gym libraries. FREE ACCESS
  • Locked
    11.  Exercise: Implementing GAN and Deep Q-Learning
    3m 40s
    Upon completion of this video, you will be able to recall the variations of generative adversarial network, implement a generative adversarial network Discriminator and Generator using Python, and implement deep… FREE ACCESS

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