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Bayesian Machine Learning in Python: A/B Testing
by Lazy Programmer Inc.- 4.6
10.5 hours on-demand video
You’ll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning. It’s also powerful, and many machine learning experts often make statements about how they “subscribe to the Bayesian school of thought”. My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch...
$14.99
The Conversational Spanish Rapid-Learning Method
by TeachMe Productions- 4.5
10 hours on-demand video
Video lectures, practice videos and quizzes to provide you with the modeling, repetition and reinforcement to learn Spanish quickly and properly. Our Spanish learning program allows you to: But after taking your course, learning finally became easy and understandable....
$21.99
Data Science: Supervised Machine Learning in Python
by Lazy Programmer Team- 4.8
6.5 hours on-demand video
Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Machine learning also raises some philosophical questions. One we’ve studied these algorithms, we’ll move to more practical machine learning topics....
$29.99
Reinforcement Learning: A Tutorial Scope of Tutorial
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learning (RL). Reinforcement learning is not a type of neural network, nor is it an alternative to neural networks. Reinforcement learning combines the fields of dynamic programming and supervised learning to yield powerful machine-learning systems....
Reinforcement Learning - University of Maryland, Baltimore ...
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Formalizing Reinforcement Learning....
Introduction to Reinforcement Learning
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Definition of reinforcement learning problem 3. Brief overview of RL algorithm types •Goals: •Understand definitions & notation •Understand the underlying reinforcement learning objective •Get summary of possible algorithms...
Lecture 1: Introduction to Reinforcement Learning
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Lecture 1: Introduction to Reinforcement Learning The RL Problem Reward Rewards Areward R t is a scalar feedback signal Indicates how well agent is doing at step t The agent’s job is to maximise cumulative reward Reinforcement learning is based on thereward hypothesis De nition (Reward Hypothesis) All goals can be described by the ....
Reinforcement Learning - Carnegie Mellon University
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Reinforcement Learning Applications Finance Portfolio optimization Trading Inventory optimization Control Elevator, Air conditioning, power grid, … Robotics Games Go, Chess, Backgammon Computer games Chatbots …...
Passive Reinforcement Learning - Courses
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Passive Reinforcement Learning Bert Huang Introduction to Artificial Intelligence. Value Iteration Passive Learning Active Learning States and rewards Transitions Decisions Observes all states and rewards in environment Observes only states (and rewards) visited by agent...
Deep Reinforcement Learning - Julien Vitay
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Deep reinforcement learning (deep RL) is the integration of deep learning methods, classically used in supervised or unsupervised learning contexts, with reinforcement learning (RL), a well-studied adaptive control method used in problems with delayed and partial feedback (Sutton and Barto, 1998)....
Hierarchical Reinforcement Learning
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tions we can still apply standard decision-making and learning methods. 4) A reinforcement learning ....