July 2024
Intermediate to advanced
650 pages
17h 23m
English
book
Deep Reinforcement Learning with Python: RLHF for Chatbots and Large Language Models
by Nimish Sanghi
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Index
A
Action
Action space
Action value functions
Activation function
Actor-critic (AC) approach
A2C
pseudocode
advantage
asynchronous advantage
implemeting A2C
Adaptability
9.a-ddpg.ipynb file
Advantage actor-critic (A2C)
actor
implementation
MC approach
pseudocode
Advantage functions
Agent
Agent Environment Cycle (AEC)
Agent learning network
Agent modeling
AlphaGo
branching factor
general approaches
MCTS
neural network training
policies
RL network
SL policy network
standard search tree
Antithetic sampling
Arcade Learning Environment (ALE)
Aroon Indicator
Artificial intelligence (AI)
definition
Generative AI
Artificial neural networks
Asynchronous advantage actor-critic (A3C)
Asynchronous Reinforcement Learning
Asynchronous version
Atari games
actions
breakout
frameskipping
observation ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.
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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.