Machine Vision, GANs, and Deep Reinforcement Learning LiveLessons, 2nd Edition
ISBN: 0136620221 | .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 6h 5m | 10.49 GB
Instructor: Jon Krohn

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.

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 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 with an interest in applying Deep Learning to natural language data
Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful



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