第一课 + 第二课（1/4）theories of deep learning
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打开封面 下载高清视频 观看高清视频 视频下载器
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论文分解器：Generalization in deep learning (合集）
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论文分解器：a critical appraisal to deep learning (更新中）
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Deep Learning Book: linear dependence, combination, span 01
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Deep Learning Book: Vectors
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Deep Learning Book: Matricies
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Deep Learning Book: 2.5 Norms
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Deep Learning Book: 2.4 linear dependence, combination, span 02
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deep learning book chp01
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Deep Learning Book: scalar
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Deep Learning Book: tensors
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deep learning book chp2 笔记02
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Deep Learning Book: 2.3 Identity and Inversion Matrices
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论文分解器：deep learning and information bottleneck principle (section3)
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Deep Learning Book: chap1.06
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Deep Learning Book: 2.2 multiplying matrices and vectors
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Deep Learning Book: chap1.04
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Deep Learning Book: chap1.03 graph
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对比pytorch, chainer (deep learning library) 更多关注
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deep learning book chapter 2 笔记03
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Stories of infection
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CNN: 4.7 what deep cnn is learning part 1
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CNN: 4.7 what deep cnn is learning part 2
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论文分解器：on the origin of deep learning (更新完section5)
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哈佛通识概率普及课（特别推荐）Fat Chance: Probability from the Ground Up by Harvard and Edx
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(EN) 第二课作业解析：regularization 02
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details of learning
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BioBits® Protein structure and function
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EN 第二课作业解析：Regularization 01
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AR introductions
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3.1 residual learning
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AlignedReID：如何理解mutual learning
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Batch_Normalization: Learning rate 更大也没关系
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如何深入交流视频内容
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AlignedReID：mutual learning 效果的实验对比
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读花书：2.9 Moore-Penrose Pseudoinverse
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复杂系统的基础之机器学习 Fundamentals of Machine Learning
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Santa Fe Institute 复杂系统公开课Complexity: An introduction
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尝试用ABM再造一些经济学的基本概念
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搜集关于高频量化的深入报道
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读花书：3.4 marginal probability