What you would learn in Machine Vision, GANs, and Deep Reinforcement Learning course?
Machine Vision, GANs Deep Reinforcement Learning LiveLessons is a brief introduction to three intriguing areas of Deep Learning today. Modern machine vision is based on machines outperforming humans in image identification, object recognition, and image segmentation tasks. Generative Adversarial Networks cast two Deep Learning networks against each one in the form of a "forger-detective" relationship, enabling the creation of stunning photos that are photorealistic and have flexible user-specific elements. Deep Reinforcement Learning has produced astonishing advances and includes most of the most well-known "artificial intelligence" breakthroughs. Deep RL is the process of training agents "agents" to become adept in a variety of "environments," enabling algorithms to surpass human performance on a range of complex tasks, including Atari video games and playing the game on a board Go and even intricate hand-manipulation tasks. In these classes, the most important theories are made real by clear explanations and interactive demonstrations using Jupyter notebooks that are hands-on. The examples include Python and superficial Keras layers from TensorFlow 2, the most loved Deep Learning library.
The Instructor's Background
Jon Krohn is Chief Data Scientist at machine learning firm untapt. He has a cult series of deep-learning tutorials published through Addison-Wesley and is the writer of the best-selling publication Deep Learning Illustrated. Jon instructs his deep-learning program in the classroom of the New York City Data Science Academy and also visits lecturers at Columbia University and New York University. He is a doctoral candidate in neuroscience from Oxford University and has published articles on machine learning in renowned journals since 2010.
Skills Level
Course Content:
- Learn the theory at the top of the pyramid and the fundamental language of machine vision, deep reinforcement learning, and generative adversarial network
- Develop state-of-the-art models to recognize images as well as object detection and image segmentation
- Architecture GANs can create stunning images that are reminiscent of hand-drawn human illustrations
- Create potent RL agents that are adept in a range of situations, including those offered by OpenAI Gym
- Run automated experiments for optimizing deep reinforcement learning agent hyperparameters, such as its artificial-neural-network configuration
- Be aware of the limits to "artificial intelligence" and how they could be overtaken shortly.
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