January 2018
Beginner to intermediate
284 pages
8h 35m
English
Content preview from Deep Learning Essentials
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Problem setup
The core setup of RL involves two components: (a) an Agent and (b) Environment. Both of these components interact dynamically with each other. For example, an agent takes a particular action that changes the existing state of the environment. The environment, based on this change, transitions to a new state and provides feedback to the agent as to how positive or negative the action taken by the agent was. This feedback is what we refer to as the weak supervision for the agent. After receiving this feedback, the agent tries to learn and optimize its future actions so that it can maximize its positive feedback. This feedback is often referred to as the reward function. After a few iterations, when the agent has learnt well, the ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.