Course details

Applied Deep Learning: Generative Adversarial Networks and Q-Learning

Applied Deep Learning: Generative Adversarial Networks and Q-Learning


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

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.



Expected Duration (hours)
0.8

Lesson Objectives

Applied Deep Learning: Generative Adversarial Networks and Q-Learning

  • 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
  • Generative Adversarial Network Variations
  • 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
  • Course Number:
    it_mlacnndj_02_enus

    Expertise Level
    Intermediate