Neural Networks Lectures Stanford

Stanford Reinforcement Learning lectures
Probing Neural Circuits with

On 22th of December I watched videos of Machine learning lectures focused on Reinforcement Learning.

Lectuer: Associate Professor Andrew Ng, Stanford University

  • Applications of Reinforcement Learning, Markov Decision Process (MDP), Defining Value & Policy Functions, Value Function, Optimal Value Function, Value Iteration, Policy Iteration (watch on YouTube)
  • Generalization to Continuous States, Discretization & Curse of Dimensionality, Models/Simulators, Fitted Value Iteration, Finding Optimal Policy (watch on YouTube)
  • Partially Observable MDPs (POMDPs), Policy Search, Reinforce Algorithm, Pegasus Algorithm, Pegasus Policy Search, Applications of Reinforcement Learning (watch on YouTube)

See other Andrew Ng’s Machine Learning courses.

Source: Master thesis - Modelling of object grasping with neural networks in the iCub humanoid robot simulator

Springer The Elements of Statistical Learning
Book (Springer)

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15 Things You Shouldn't Be Paying For

2010-08-19 09:27:17 by YouUngratefulTurds

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"With repeated presentations of a stimulus," writes Eagleman, "a sharpened representation or a more efficient encoding is achieved in the neural network coding for the object." Once the brain has learned to recognize the ... Marcel Proust's effort to ...

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For example, the perceived brightness of an area can be greatly influenced by the luminance of the surrounding areas: a gray patch on a dark background can appear as bright as a darker patch on a bright background [2]. ... The advantage of using D K L ...

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