Applied Deep Learning: Generative Adversarial Networks and Q-Learning

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.