• 投稿

林轩田机器学习基石(国语)

44.1万
8014
2017-07-22 16:15:09
4231
2181
2.4万
1920
https://www.youtube.com/playlist?list=PLXVfgk9fNX2I7tB6oIINGBmW50rrmFTqf Machine Learning Foundations (機器學習基石)
机器学习
AI
人工智能
公开课
爱可可-爱生活 发消息
新浪微博 @爱可可-爱生活 http://weibo.com/fly51fly
别再瞎养生！基因早就告诉你答案
好运小狗ye
2557 96
视频选集
（1/65）
1.1 The Learning Problem - Course Introduction
09:50
1.2 The Learning Problem - What Is Machine Learning
18:30
1.3 The Learning Problem - Applications of Machine Learning
18:58
1.4 The Learning Problem - Components of Learning
11:46
1.5 The Learning Problem - Machine Learning and Other Fields
10:22
2.1 Learning to Answer Yes_No - Perceptron Hypothesis Set
15:12
2.2 Learning to Answer Yes_No - Perceptron Learning Algorithm
19:47
2.3 Learning to Answer Yes_No - Guarantee of PLA
12:38
2.4 Learning to Answer Yes_No - Non-Separable Data
12:56
3.1 Types of Learning - Learning with Different Output Space
16:57
3.2 Types of Learning - Learning with Different Data Label
18:14
3.3 Types of Learning - Learning with Different Protocol
11:10
3.4 Types of Learning - Learning with Different Input Space
14:14
4.1 Feasibility of Learning - Learning is Impossible
13:03
4.2 Feasibility of Learning - Probability to the Rescue
11:33
4.3 Feasibility of Learning - Connection to Learning
16:47
4.4 Feasibility of Learning - Connection to Real Learning
18:07
5.1 Training versus Testing - Recap and Preview
13:19
5.2 Training versus Testing - Effective Number of Lines
15:27
5.3 Training versus Testing - Effective Number of Hypotheses
16:18
5.4 Training versus Testing - Break Point
07:44
6.1 Theory of Generalization - Restriction of Break Point
13:47
6.2 Theory of Generalization - Bounding Function - Basic Cases
06:57
6.3 Theory of Generalization - Bounding Function - Inductive
14:48
6.4 Theory of Generalization - A Pictorial Proof
16:02
7.1 The VC Dimension - Definition of VC Dimension
12:34
7.2 The VC Dimension - VC Dimension of Perceptrons
13:28
7.3 The VC Dimension - Physical Intuition of VC Dimension
06:11
7.4 The VC Dimension - Interpreting VC Dimension
17:14
8.1 Noise and Error - Noise and Probabilistic Target
16:30
8.2 Noise and Error - Error Measure
15:11
8.3 Noise and Error - Algorithmic Error Measure
13:48
8.4 Noise and Error - Weighted Classification
16:55
9.1 Linear Regression - Linear Regression Problem
09:37
9.2 Linear Regression - Linear Regression Algorithm
20:04
9.3 Linear Regression - Generalization Issue
20:35
9.4 Linear Regression - for Binary Classification
11:24
10.1 Logistic Regression - Logistic Regression Problem
14:02
10.2 Logistic Regression - Logistic Regression Error
15:59
10.3 Logistic Regression - Gradient of Logistic Regression Error
15:39
10.4 Logistic Regression - Gradient Descent
19:19
11.1 Linear Models for Classification - Binary Classification
20:59
11.2 Linear Models for Classification - Stochastic Grad. Descent
11:40
11.3 Linear Models for Classification - Multiclass via Logistic
14:19
11.4 Linear Models for Classification - Multiclass via Binary
11:36
12.1 Nonlinear Transformation - Quadratic Hypotheses
23:21
12.2 Nonlinear Transformation - Nonlinear Transform
09:53
12.3 Nonlinear Transformation - Price of Nonlinear Transform
15:38
12.4 Nonlinear Transformation - Structured Hypothesis Sets
09:37
13.1 Hazard of Overfitting - What is Overfitting
10:19
13.2 Hazard of Overfitting - The Role of Noise and Data Size
13:36
13.3 Hazard of Overfitting - Deterministic Noise
14:08
13.4 Hazard of Overfitting - Dealing with Overfitting
10:50
14.1 Regularization - Regularized Hypothesis Set
18:45
14.2 Regularization - Weight Decay Regularization
24:09
14.3 Regularization - Regularization and VC Theory
08:16
14.4 Regularization - General Regularizers
13:29
15.1 Validation - Model Selection Problem
15:34
15.2 Validation - Validation
13:25
15.3 Validation - Leave-One-Out Cross Validation
16:07
15.4 Validation - V-Fold Cross Validation
10:42
16.1 Three Learning Principles - Occam's Razor
09:37
16.2 Three Learning Principles - Sampling Bias
11:51
16.3 Three Learning Principles - Data Snooping
12:29
16.4 Three Learning Principles - Power of Three
08:50

【进来许愿】今年年报不收礼，收礼只收...
点击贡献创意>>

快速傅里叶变换(FFT)——有史以来最巧妙的算法？
爱可可-爱生活
39.4万 1447

AI让人类变强了吗？一场关于进化的错觉
爱可可-爱生活
2001 0

北邮《AI原生开发》第2讲：两万行你不认识的代码
爱可可-爱生活
1829 0

AI都会写代码了，我们还要学编程吗？
爱可可-爱生活
2826 1

李宏毅对抗生成网络(GAN)国语教程(2018)
爱可可-爱生活
32.2万 6332

AI裁员陷阱：一场所有人都在输的理性游戏
爱可可-爱生活
6979 27

机器学习技法（林轩田）
爱可可-爱生活
16.9万 3508

北邮《AI原生开发》预告片
爱可可-爱生活
2117 0

(企业)智能体落地方法论——从政策红利到业务增长的最短路径
爱可可-爱生活
1281 0

哈佛大学CS50《计算机导论》课程(2019)
爱可可-爱生活
20.8万 1734

AI智能体赋能课堂教学——技术很简单，难的是想法
爱可可-爱生活
11.6万 68

北邮《AI原生开发》第3讲：AI原生开发的七环法度
爱可可-爱生活
1074 0

李宏毅机器学习(2017)
爱可可-爱生活
94.2万 2.4万

从AGI到ASI：人工智能的下一个跨越
爱可可-爱生活
3358 2

北邮《AI原生开发》第1讲：三句话之后，一个软件出现了
爱可可-爱生活
2155 1

【双语字幕】MIT《数据分析、信号处理和机器学习中的矩阵方法》课程 (Spring 2018) by Gilbert Strang
爱可可-爱生活
14.8万 618

AI怎么学会举一反三？——泛化的秘密
爱可可-爱生活
1949 0

AI第一性原理
爱可可-爱生活
2024 0

AI写代码，程序员要失业了？Linux之父说：你们都想错了
爱可可-爱生活
4017 4

李宏毅《深度学习人类语言处理》国语课程(2020)
爱可可-爱生活
6.9万 978
展开
和平精英新版本雪战冬日正式开启！
客服
顶部
赛事库 课堂 2021拜年纪