Jon Krohn – Deep Reinforcement Learning And GANs Advanced Topics in Deep Learning

Jon Krohn – Deep Reinforcement Learning And GANs Advanced Topics in Deep Learning

Check it out: Jon Krohn – Deep Reinforcement Learning and GANs: Advanced Topics in Deep Learning

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6+ Hours of Video Instruction
An intuitive introduction to the latest developments in Deep Learning.

Overview
Deep Reinforcement Learning and GANs LiveLessons is an introduction to two of the most exciting topics in Deep Learning today. Generative Adversarial Networks cast two Deep Learning networks against each other in a “forger-detective” relationship, enabling the fabrication of stunning, photorealistic images with flexible, user-specifiable elements. Deep Reinforcement Learning has produced equally surprising advances, including the bulk of the most widely-publicized “artificial intelligence” breakthroughs. Deep RL involves training an “agent” to become adept in given “environments,” enabling algorithms to meet or surpass human-level performance on a diverse range of complex challenges, including Atari video games, the board game Go, and subtle hand-manipulation tasks. Throughout these lessons, essential theory is brought to life with intuitive explanations and interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow, the most popular Deep Learning library.

Skill Level
– Intermediate

Learn How To
– Understand the high-level theory and key language around deep reinforcement learning and generative adversarial networks
– Architect GANs that create convincing images in the style of human-drawn illustrations
– Build deep RL agents that become adept at performing in a wide variety of environments, such as those provided by OpenAI Gym
– Run automated experiments for optimizing deep reinforcement learning agent parameters, such as its artificial-neural-network configuration
– Appreciate what the current limitations of “artificial intelligence” are and how they may be overcome in the near future

Who Should Take This Course
– Perfectly suited to software engineers, data scientists, analysts, and statisticians who want to further their understanding of deep learning.
– Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful.
– Previous experience with statistics or machine learning is not necessary.