| Sumario: | 6 Hours of Video Instruction An intuitive introduction to the latest superhuman capabilities facilitated by Deep Learning. Overview Machine Vision, GANs, Deep Reinforcement Learning LiveLessons is an introduction to three of the most exciting topics in Deep Learning today. Modern machine vision involves automated systems outperforming humans on image recognition, object detection, and image segmentation tasks. 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 straightforward Keras layers in TensorFlow 2, the most popular Deep Learning library. About the Instructor Jon Krohn is Chief Data Scientist at the machine learning company untapt. He presents a popular series of deep learning tutorials published by Addison-Wesley and is the author of the bestselling book Deep Learning Illustrated . Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy, as well as guest lecturing at Columbia University and New York University. He holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading journals since 2010. Skill Level Intermediate Learn How To Understand the high-level theory and key language around machine vision, deep reinforcement learning, and generative adversarial networks Create state-of-the art models for image recognition, object detection, and image segmentation 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 hyperparameters, such as its artificial-neural-network...
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