MetaLearning Learning Note - 4

• optimization based on meta learning
• non-parametric few-shot learning
• properties of meta learning algorithms.
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Reinforcement Learning Day 4 (Finite Markov Decision Processes’s Coursera Video Notes)

• Specifying Policies
• Value Functions
• Action-value function
• Bellman Equation Derivation
• Intuition - Bellman Eqaution
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Reinforcement Learning Day 3 (Finite Markov Decision Processes)

• Return, Policy and Value Function
• Optimal Policies and Optimal Value Functions
• Coursera False Questions
• Optimality and Approximation
• Summary
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最近又再捣鼓捣鼓自己HKKB的键盘，一直没有整明白自己的HHKB后面那一串开关的用处，后面写了一串英文介绍也没弄懂是怎么使用。经过仔细探究发现主要要的三个用途。

MetaLearning Learning Note - 2

Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 3 - Optimization-Based Meta-Learning

• Recap the probabilistic formulation of meta-learning
• general recipe of meta-learning algorithm
• black box adaption appraoches
• optimization-based meta-learning algorithm

Preface

I choose to learn meta-learning through the stanford’s coursework. Prof.Song and Dr.Xu ask me do some work around reinforcement learning and meta learning. Therefore, I choose to learn meta learning from stanford’s coursework by Chelsea Finn.

Reinforcement Learning Day 2 (Multi-armed Bandits)

• Q* Formula
• Nonstationary problem
• Optimistic Initial Value
• Gradient Bandits Problem
• Associative Search
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Insights for co-run HPC AND GPU taks - Day5 Try some examples for co-run HPC and ML tasks

• Build the production environment
• NPB
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