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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

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    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- \# C3 v7 p# b, S* S
    1 - 2 - What is Machine Learning_ (7 min).mkv
    : Q# Z! I" s9 u& Y% _( d  u1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E. K0 ]1 A# u/ c$ h$ e
    1 - 4 - Unsupervised Learning (14 min).mkv
    ( o' P5 r; t) o8 `. n% }% @  X2 - 1 - Model Representation (8 min).mkv9 f# w2 i4 t& e/ f
    2 - 2 - Cost Function (8 min).mkv7 c2 `/ ~) m; r+ Q! w  w/ t3 l
    2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |  S; w( R- z/ J+ |$ V8 O
    2 - 4 - Cost Function - Intuition II (9 min).mkv8 L8 j8 Q9 i6 h; \1 ^6 f
    2 - 5 - Gradient Descent (11 min).mkv( S3 B& f4 f8 |
    2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p
    5 N/ l8 p9 m8 Z5 Q7 g2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |
    0 D6 n1 q* \6 C% V9 y2 l$ j: j2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b/ L- F+ V* c4 L) v8 q7 d3 H
    3 - 1 - Matrices and Vectors (9 min).mkv
    6 p0 q9 `8 w1 y7 ]+ _8 S7 J2 ^3 S4 I3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?
    1 n3 D( |4 c3 @6 h7 v3 - 3 - Matrix Vector Multiplication (14 min).mkv
    6 ]# W, _, |1 d3 - 4 - Matrix Matrix Multiplication (11 min).mkv: }5 [3 z5 M: ~% ]# M- r4 \
    3 - 5 - Matrix Multiplication Properties (9 min).mkv; _0 E/ \# B; T  ]2 t
    3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f
    % b/ y" \8 o" O+ O7 O4 - 1 - Multiple Features (8 min).mkv
    : ?% O8 H  u0 K$ s" s8 }4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv$ R! b- K% f! W! ]% J: \, l
    4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv
    : B& m% a" |  J* Z* y& \4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv
    6 y: e" I% }  P0 H; v  U4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m$ j& X  C- a9 A6 Z" G" K
    4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X
    * Y( R# l" I! i4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |; ~! z3 U1 c$ Q9 k& V5 n
    5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F
    8 _( C* c: `3 Q- k$ V. I8 Y5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N
    ) y' p! o- i2 `2 p5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z
    $ u% ~7 i7 R3 @# h  y# J+ i/ M5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g
    . D  v8 R( i7 o$ C  N, ^. n5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u" B' ^# G1 }% ]/ y" E
    5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \9 a; p0 b& ^  N9 ?5 D7 V
    5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y
    / G3 Q$ H8 V) S9 y& |6 - 1 - Classification (8 min).mkv
    - {' P6 M6 e( C4 ^& ~6 ]6 - 2 - Hypothesis Representation (7 min).mkv6 l) y4 f$ T2 |5 Y4 K/ o
    6 - 3 - Decision Boundary (15 min).mkv
    " m7 ?: b- k3 p# x8 @' B6 - 4 - Cost Function (11 min).mkv
    $ {* ~4 X2 M/ V8 h5 b6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y" q4 A5 c: d; P& B9 W" j
    6 - 6 - Advanced Optimization (14 min).mkv/ l( h' }8 w8 m
    6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z
    6 i2 c' G# r5 N! f% `8 A7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z
    . k% U* J7 W1 e" N  G1 I7 - 2 - Cost Function (10 min).mkv8 H- Q) Q" F2 x0 u; A6 c+ z  n
    7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w
    1 ^% h+ D# S" k4 C7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A
    . e4 E; K5 s7 E+ Z$ l8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e' _) S+ |+ s7 }* @) y' y
    8 - 2 - Neurons and the Brain (8 min).mkv" h" {& O2 n9 a; @) J- F- I
    8 - 3 - Model Representation I (12 min).mkv
    . f2 [$ l/ z. |3 E8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H
    1 O* \, A1 Z7 f# A, S) ~+ s$ K8 - 5 - Examples and Intuitions I (7 min).mkv
    + D4 d; s# M! X8 - 6 - Examples and Intuitions II (10 min).mkv
    # z  L, |$ |1 Q; j# J4 A2 L8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z
    # U5 Y% o6 q* u+ Q* X2 {8 H9 - 1 - Cost Function (7 min).mkv* M+ K9 L: K5 i. W! L9 U* s
    9 - 2 - Backpropagation Algorithm (12 min).mkv
    * A( h, [! V4 o3 m& k( Q0 o9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @
    8 {( O9 e7 [/ o" i9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L
    * M3 p6 M- `! P5 b8 e9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U
    , J5 e: {7 x# E, d6 j0 j6 N" [9 - 6 - Random Initialization (7 min).mkv
    ; F0 i  A+ s2 n3 E" w9 - 7 - Putting It Together (14 min).mkv
    + y6 J5 J8 W9 b9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E! G* ]- G) K  e4 z' h' m0 v* F
    10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e0 c; e4 F$ {4 [# M, G! q+ _, x' x
    10 - 2 - Evaluating a Hypothesis (8 min).mkv
    . |% b1 m1 i. A; e: b10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv
    . u  @& G' M3 `+ q, q10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G
    4 ~+ d( z6 b( ]3 {7 j10 - 5 - Regularization and Bias_Variance (11 min).mkv3 f' f1 V) \9 A# x6 u
    10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M3 H( s7 |% `+ M9 ^$ B! ?
    10 - 7 - Deciding What to Do Next Revisited (7 min).mkv% M: R! q$ l! [) [6 q0 e/ P
    11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m
    ' d* C) [9 p* g, e5 x& G! v. i11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v
    ! m/ f1 x; X8 ~9 _11 - 3 - Error Metrics for Skewed Classes (12 min).mkv! }9 j) |2 M9 k/ i  H
    11 - 4 - Trading Off Precision and Recall (14 min).mkv
    9 d" k2 w6 B: {/ S3 [$ x  Q5 T" f11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a
    + S% S. |. |1 }8 T/ J- [! X7 R6 ?/ V12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c
    + U1 q) y1 W& Y; T12 - 2 - Large Margin Intuition (11 min).mkv
    8 y. K9 j: @8 w) N$ h4 C/ p; R7 n12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv
    6 _% L* H0 J/ N5 ]9 O0 M) |12 - 4 - Kernels I (16 min).mkv
    3 p; E, n% c6 M: ]2 x& V. L) L0 [12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M) Y) v/ E+ n1 ?* S7 J: _
    12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j
    9 J0 |' ]6 r: z! u: v13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d9 n! }; @9 k4 q' x5 M3 W# W. W" U9 Q
    13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k
    ! w6 q! Z4 A$ f+ U% J6 G! j13 - 3 - Optimization Objective (7 min)(1).mkv0 E% |8 K0 v2 i' c
    13 - 3 - Optimization Objective (7 min).mkv
    & a4 d, p# u3 k" G+ D5 K13 - 4 - Random Initialization (8 min).mkv; u! G4 S: a+ i5 `9 n, ?) i
    13 - 5 - Choosing the Number of Clusters (8 min).mkv- T7 N1 \, ~' U2 u
    14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q% `$ `; q' s. w: m7 I
    14 - 2 - Motivation II_ Visualization (6 min).mkv
    7 N; ^2 f5 a0 Z* T* O8 c& E) D14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv) k% M& a, m! |0 Y  }7 b5 z
    14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [& t, i2 O5 m" Z. J" L2 i" |6 R
    14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g
    ; P6 C" C  J. D5 Q8 M0 t8 Q14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @1 ^4 x1 x8 `9 B9 o. R0 @, @% L3 G
    14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W
    " ~: U, p$ j5 g3 q6 Y, z15 - 1 - Problem Motivation (8 min).mkv
    # c1 M; E1 P% q9 k' i15 - 2 - Gaussian Distribution (10 min).mkv
    $ v# x( T: H& a! e1 f- r15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A% o# y" J$ D5 d/ G# y
    15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
    + @9 l$ g3 W' v: z* Z3 o0 h% f15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv
    % o) r+ S0 c: @7 m' b' Y15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q
    # U3 M7 O% X! n7 \6 ?  A15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U
    4 U6 X" s' m7 g1 @$ Z, u15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e- J$ K  j" K, X7 a5 M8 V
    16 - 1 - Problem Formulation (8 min).mkv
    1 A$ B2 a0 F% _/ F8 m+ Q5 x16 - 2 - Content Based Recommendations (15 min).mkv: H0 K! ?' c7 w' U3 A
    16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e# Q% p8 s/ x7 {+ W: i6 G
    16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]
    ' u4 e" Q# C! L! n' ?16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv' |/ x, r( z' @7 f
    16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv! E# O3 I# z) x: ?
    17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R
    ! b8 W+ Y0 l6 j17 - 2 - Stochastic Gradient Descent (13 min).mkv
    " i1 C4 K9 A* E/ x17 - 3 - Mini-Batch Gradient Descent (6 min).mkv1 \7 q! N+ M- e+ j0 y# j1 h
    17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f0 W1 w  v$ S* b2 l5 e9 O
    17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?; N& t0 R( Y$ z4 v# U
    17 - 6 - Map Reduce and Data Parallelism (14 min).mkv, T% _( t( s7 C  k8 m
    18 - 1 - Problem Description and Pipeline (7 min).mkv( N8 Z) O) }( `7 P% n3 `9 V/ l* S
    18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d! n. {% v' i% N* j. B& ~% [- @
    18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L0 b9 r- K- x8 j- T; F! o
    18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv
    % p9 M! a7 e. z$ D4 i19 - 1 - Summary and Thank You (5 min).mkv/ [! k7 z3 m# Q3 M: r* v" s$ B) W: i5 N# _
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    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
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    发表于 2017-10-25 06:58:28 | 显示全部楼层
    O(∩_∩)O谢谢
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    发表于 2017-10-25 11:53:07 | 显示全部楼层
    thanks!!!
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    感谢分享
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