设为首页收藏本站

自学IT吧论坛

 找回密码
 立即注册
搜索
查看: 3763|回复: 123

斯坦福大学吴恩达机器学习视频教程 带中英文字幕学习笔记

  [复制链接]
  • TA的每日心情

    12 小时前
  • 签到天数: 1340 天

    [LV.10]以坛为家III

    1176

    主题

    2623

    帖子

    5404

    积分

    管理员

    Rank: 9Rank: 9Rank: 9

    积分
    5404
    发表于 2017-10-25 01:16:59 | 显示全部楼层 |阅读模式
    1 - 1 - Welcome (7 min).mkv
    ; v  N+ y) C/ D/ }% a7 E3 u1 - 2 - What is Machine Learning_ (7 min).mkv
    6 j& n9 O4 M% F- m, W9 d1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E
    . ]/ e' W% @, j0 Q9 M6 ^1 - 4 - Unsupervised Learning (14 min).mkv0 X! Q  n5 n/ a; N0 `
    2 - 1 - Model Representation (8 min).mkv$ w* c; j5 ~  d" s$ S
    2 - 2 - Cost Function (8 min).mkv
    . k% j) A- E' U$ w# W8 C7 \2 i) `2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |
    ( ]+ ?* D3 ?" N2 - 4 - Cost Function - Intuition II (9 min).mkv" w$ s* c" k, w: I% x2 X
    2 - 5 - Gradient Descent (11 min).mkv
    ; m0 c; S* w4 [8 ~& v2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p2 \- j1 i# j$ C; n
    2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |
    , e- h6 J2 l/ m  }2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b) o: e2 _( z$ p8 X- G9 f
    3 - 1 - Matrices and Vectors (9 min).mkv7 d+ C! p% M' j+ f; g7 x5 s
    3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?
    ; b, B/ P$ L+ {+ Y) S0 H3 - 3 - Matrix Vector Multiplication (14 min).mkv7 v/ N) ^# |( G: W5 b
    3 - 4 - Matrix Matrix Multiplication (11 min).mkv1 C9 m; a. o+ [
    3 - 5 - Matrix Multiplication Properties (9 min).mkv. [& [; {) g2 z0 @
    3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f
    , ~6 V- b& j% d+ O9 K; r; u4 - 1 - Multiple Features (8 min).mkv
    % Y. N; `8 o4 {% a4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv& S% B# t$ ?& A/ \
    4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv
    # n' H/ F1 u# U9 O( V* @4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv( A' Q6 B6 `4 |$ q
    4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m
    . ~9 C. G9 V, a8 X4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X
    7 K; h- c8 H$ a4 p' S4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |. |* c5 h, u6 q4 G
    5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F$ Y" Z  P* c) u
    5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N2 E# H8 Q9 F; P+ @; C% \
    5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z" h2 y! x# X( J" Y3 O0 h- \
    5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g
    - Q0 z8 N: A5 Y6 A/ J7 ~3 Z5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u0 z1 l& e3 b6 @) F- _  l
    5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \
    + q0 u. [, L7 P$ W4 F" r9 L4 M5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y3 l* ]: w2 U+ T3 U$ S" q
    6 - 1 - Classification (8 min).mkv
    : O0 ^7 y; V; B6 z, J8 \6 - 2 - Hypothesis Representation (7 min).mkv% ]) m. W7 ^  _2 y/ s# M6 x
    6 - 3 - Decision Boundary (15 min).mkv
    8 k& [- |  y% e" X+ H# f+ X6 - 4 - Cost Function (11 min).mkv
    5 Z8 r! ^: Z* r- V% B$ P9 R6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y
    9 n3 V' e0 I6 U& R& n' q) |6 - 6 - Advanced Optimization (14 min).mkv5 N5 z- A, B8 h3 F+ b
    6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z
    1 a% L6 P6 I; ~' d' h! [4 j* T+ F7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z
    : _- O) S  T/ V' Q- g7 - 2 - Cost Function (10 min).mkv
    2 ?3 s1 O" R* B& [: S% s7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w& T* A9 |( O) H# P
    7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A
    ' K- y; T6 ^2 v. q7 @6 X# P8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e: [. E3 O/ \5 D' G
    8 - 2 - Neurons and the Brain (8 min).mkv
    & t. A* c5 w, W! {8 - 3 - Model Representation I (12 min).mkv; H& E+ p0 z7 M* B
    8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H
    % M- H) F7 Q: B$ Q' }- X8 - 5 - Examples and Intuitions I (7 min).mkv7 m  R+ c1 x6 W8 J! D
    8 - 6 - Examples and Intuitions II (10 min).mkv; _4 v& H/ g( T! s! f
    8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z( `2 M# c% R4 d$ Y
    9 - 1 - Cost Function (7 min).mkv
    # F- K& `3 @* R  I" W* x9 - 2 - Backpropagation Algorithm (12 min).mkv
    # N- K) f3 V, X  E7 I9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @/ l) y4 J0 I1 h$ Q
    9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L- V* T6 [5 t9 E  L3 N
    9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U5 R/ {" a9 z! c& N$ S
    9 - 6 - Random Initialization (7 min).mkv
    / T$ |* {  f1 N1 k9 - 7 - Putting It Together (14 min).mkv
    ; [4 `0 @8 R# x! ]6 U9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E- P6 u- Z. L! v; W4 d+ B4 W# H& `0 j
    10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e' L, `: f6 O, ~! u3 _* p
    10 - 2 - Evaluating a Hypothesis (8 min).mkv7 @; T$ p' `# W
    10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv' j: l/ p/ ]4 d* w4 h) X
    10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G
      \. a+ X2 H. m0 m0 W& [10 - 5 - Regularization and Bias_Variance (11 min).mkv8 i) B5 s- k- C3 `6 `
    10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M+ j  I$ T8 {" N2 v. S# F+ I0 C) U
    10 - 7 - Deciding What to Do Next Revisited (7 min).mkv
    5 U8 @$ I6 c" W4 {& [11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m2 ?" @4 h. k2 _5 \: j) u* U# h
    11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v
    6 w7 ?0 ]- ?+ i/ w) j6 r11 - 3 - Error Metrics for Skewed Classes (12 min).mkv
    1 K& _" |+ i4 q0 H; R. }0 C, d7 S$ m11 - 4 - Trading Off Precision and Recall (14 min).mkv
    5 y0 H$ n2 D7 P2 h; w( K11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a4 V/ X, [) v* ~3 k8 E
    12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c4 C( S  }" b, P- s2 G1 x& x
    12 - 2 - Large Margin Intuition (11 min).mkv
    - E9 O0 L( U0 z/ K3 Z$ `1 J12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv
    4 E1 T8 O+ A5 N! E8 z7 Y, J12 - 4 - Kernels I (16 min).mkv
    , _' I6 x' k% q/ c+ X$ ^12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M
    5 i: G  x% Q0 C% q12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j
    5 G" J0 B0 {* Z% h9 ]9 A% m/ N13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d& |$ n! @+ O! W' k9 E7 K
    13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k: y1 t& J1 n8 ]
    13 - 3 - Optimization Objective (7 min)(1).mkv8 I) U6 E: }1 |$ I( @4 h
    13 - 3 - Optimization Objective (7 min).mkv
    . ?( I% o) D1 m8 x) U" z) w13 - 4 - Random Initialization (8 min).mkv
    # {  o+ O3 `3 @, y13 - 5 - Choosing the Number of Clusters (8 min).mkv! Z9 \* I0 G/ a- E' C7 G
    14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q8 I% C- c1 M4 C3 k' Q% }6 I
    14 - 2 - Motivation II_ Visualization (6 min).mkv
      m& i, X- B0 `$ m5 H14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv2 F3 f; C2 a! X5 w: j
    14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [
    * H+ {: h  k& }- G4 k0 H9 p. y14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g0 u# h8 W, G# t' c' w$ j2 c
    14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @8 a5 d8 q0 H+ ^6 e$ h
    14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W/ B- R/ Z; {# {$ \$ M% O
    15 - 1 - Problem Motivation (8 min).mkv9 S/ H4 V/ s' j5 J+ @' P0 U8 _+ Q
    15 - 2 - Gaussian Distribution (10 min).mkv% }% u# F/ p' q+ e5 o5 d, O
    15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A3 y) l' S5 O1 M9 t$ M% A4 S0 L
    15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv, U# O! r6 \  N% n# R
    15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv
    2 Z8 i- h  t$ l; x. N15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q8 o% ~- D7 {% U5 B, b6 E
    15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U
    + o; C9 ~% i+ \3 w15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e% C, ^" }3 S4 E
    16 - 1 - Problem Formulation (8 min).mkv
    1 J. }' m2 z/ O16 - 2 - Content Based Recommendations (15 min).mkv
    " w8 L% _/ L: K4 |" g2 H16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e
    ) D7 h0 M2 t4 U* d2 N: X+ U. ]16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]0 ]! X& T* J% F% s# {/ B
    16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv  p/ ?7 X2 [7 t* h3 X
    16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv0 m: A7 s3 @( s# k
    17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R
    1 J2 f7 U+ p- _: J3 w3 c17 - 2 - Stochastic Gradient Descent (13 min).mkv
    2 i3 e+ a. c3 a17 - 3 - Mini-Batch Gradient Descent (6 min).mkv
    6 d! l- r! I. r$ G" u17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f
    & J4 _9 f1 z( H  v' I1 x17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?
    0 K" Q6 |/ D) D0 ^17 - 6 - Map Reduce and Data Parallelism (14 min).mkv
    $ N" W( M5 @* K18 - 1 - Problem Description and Pipeline (7 min).mkv
    3 J3 T3 X4 ~9 O- ^0 T6 b18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d
    / h+ k7 l0 X* Y* P9 W: h18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L) X( d* C+ I/ G
    18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv
    5 U8 @- i; ~0 C) [- c4 h19 - 1 - Summary and Thank You (5 min).mkv/ [! k7 z3 m# Q3 M: r* v& k# u/ j+ P6 b9 N
    相关pdf
    0 z2 F! Z! b$ p) Q相关ppt$ a" l" O+ e8 _! T" M2 d
    中英文字幕.rar
    7 B; }# c$ N% F# }1 Q9 z' z" K如何添加中文字幕.docx
    ( O4 M$ ^7 p: `3 i. [* [, m+ i教程和个人学习笔记完整版
    7 T* K" t' Q8 s9 u3 L' y机器学习课程源代码
    , @3 q, ]8 U- C0 v9 Q3 K/ X4 z) j: `) W) `: d

    ! D/ l/ d$ [% g9 Z% b6 L6 f* [1 E! R5 w4 w

    * R- ^1 W" E8 [$ M. Q5 ~! O; \链接:$ c9 _/ v3 Q& h) v& A3 u5 o
    游客,如果您要查看本帖隐藏内容请回复

    # r' Z9 K0 z3 s" F0 m* A
  • TA的每日心情
    奋斗
    2019-9-30 22:03
  • 签到天数: 400 天

    [LV.9]以坛为家II

    2

    主题

    677

    帖子

    1948

    积分

    永久VIP会员

    积分
    1948
    发表于 2017-10-25 16:50:33 | 显示全部楼层
    斯坦福大学吴恩达机器学习视频教程 带中英文字幕学习笔记
  • TA的每日心情
    擦汗
    3 天前
  • 签到天数: 786 天

    [LV.10]以坛为家III

    2

    主题

    1722

    帖子

    4624

    积分

    永久VIP会员

    积分
    4624
    发表于 2017-10-25 06:58:28 | 显示全部楼层
    O(∩_∩)O谢谢
  • TA的每日心情
    开心
    2020-11-16 23:26
  • 签到天数: 150 天

    [LV.7]常住居民III

    0

    主题

    476

    帖子

    1275

    积分

    永久VIP会员

    积分
    1275
    发表于 2017-10-25 09:11:07 | 显示全部楼层
  • TA的每日心情
    开心
    2020-12-13 17:22
  • 签到天数: 674 天

    [LV.9]以坛为家II

    3

    主题

    1097

    帖子

    3085

    积分

    永久VIP会员

    积分
    3085
    发表于 2017-10-25 10:17:07 | 显示全部楼层
    222
  • TA的每日心情

    2020-12-9 22:34
  • 签到天数: 584 天

    [LV.9]以坛为家II

    4

    主题

    1172

    帖子

    3248

    积分

    永久VIP会员

    积分
    3248
    发表于 2017-10-25 11:53:07 | 显示全部楼层
    thanks!!!
  • TA的每日心情
    开心
    2020-9-6 19:40
  • 签到天数: 103 天

    [LV.6]常住居民II

    1

    主题

    393

    帖子

    1015

    积分

    永久VIP会员

    积分
    1015
    发表于 2017-10-25 14:02:00 | 显示全部楼层
    感谢分享
  • TA的每日心情

    2020-6-6 11:15
  • 签到天数: 359 天

    [LV.8]以坛为家I

    0

    主题

    696

    帖子

    1934

    积分

    永久VIP会员

    积分
    1934
    发表于 2017-10-25 15:56:05 | 显示全部楼层
  • TA的每日心情
    奋斗
    2020-10-12 15:35
  • 签到天数: 256 天

    [LV.8]以坛为家I

    0

    主题

    489

    帖子

    1422

    积分

    永久VIP会员

    积分
    1422
    发表于 2017-10-25 22:39:45 | 显示全部楼层
    666666666666
  • TA的每日心情
    奋斗
    2019-9-19 09:25
  • 签到天数: 533 天

    [LV.9]以坛为家II

    1

    主题

    1937

    帖子

    4592

    积分

    禁止访问

    积分
    4592
    发表于 2017-10-26 00:14:44 | 显示全部楼层
    提示: 作者被禁止或删除 内容自动屏蔽
    您需要登录后才可以回帖 登录 | 立即注册

    本版积分规则

    来自学IT吧,高薪等你拿! 立即登录 立即注册
    在线咨询
    在线咨询
    zxit_8@qq.com

    QQ|Archiver|小黑屋|自学IT吧    

    GMT+8, 2021-1-28 14:23 , Processed in 0.107802 second(s), 31 queries , Gzip On.

    © 2014-2017 自学IT吧论坛

    快速回复 返回顶部 返回列表