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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).mkv0 Z/ L5 z1 G) j; n) z
    1 - 2 - What is Machine Learning_ (7 min).mkv" z7 y6 t( G5 i) }
    1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E
    7 p, {+ P. _$ y9 p1 - 4 - Unsupervised Learning (14 min).mkv1 s7 `- F8 T' f6 @# N$ V$ |# m
    2 - 1 - Model Representation (8 min).mkv9 i* V: |0 H( V& M/ j
    2 - 2 - Cost Function (8 min).mkv; z6 U) L2 h* C9 ^
    2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |
    2 ^  m: G% K' U9 K/ _$ }2 - 4 - Cost Function - Intuition II (9 min).mkv! W. O9 q2 L! I
    2 - 5 - Gradient Descent (11 min).mkv
    # a- f* _7 x8 T& K2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p
    - @3 D6 r* V* y2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |3 @1 H" S+ P/ g7 E& @6 v- r
    2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b) A. U% d, ?! X& L& X3 N! u2 [
    3 - 1 - Matrices and Vectors (9 min).mkv
    0 o+ J8 F, \% _# V0 C3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?
    5 z, j. J  k( t9 p/ a3 - 3 - Matrix Vector Multiplication (14 min).mkv
    1 _, N/ F4 |: v: l( u3 - 4 - Matrix Matrix Multiplication (11 min).mkv
    $ U% k: l0 b) @* T3 - 5 - Matrix Multiplication Properties (9 min).mkv
    9 S+ A: h( a* t+ f; j3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f
    / f3 W! P9 s) H8 ~4 - 1 - Multiple Features (8 min).mkv
    : V  ?9 V& f% m- J% n8 s* s4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv) Z/ N  R' B  Q1 l3 u) L
    4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv
    ) v- Y0 L6 B; V1 t" n4 {0 m4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv% g5 C# M# v% N7 R! X% u
    4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m, C/ I) |- J6 w& o2 g% b
    4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X
    . ~: h% X8 ?% I! q. Z4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |
    ' y/ `- ?" I1 _. O5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F
    0 i+ h1 A# e# s; N- ]5 S# q5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N
    + j: T4 L" X& Q+ R* [5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z) f: i/ H3 \# s) o$ U7 q
    5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g
    . J! [/ u8 P8 _; h2 e$ [5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u
    1 |0 L6 M# S9 I/ N. N9 J7 P5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \7 \. J  R7 P$ R' U! |
    5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y2 g+ @! x8 q% ]5 X9 U, \
    6 - 1 - Classification (8 min).mkv$ p4 p! H/ }) ~7 t+ r- E9 G1 a
    6 - 2 - Hypothesis Representation (7 min).mkv5 f- L* B# y8 L' Q9 u
    6 - 3 - Decision Boundary (15 min).mkv) r! D5 r9 j9 k1 I. I
    6 - 4 - Cost Function (11 min).mkv4 Z% m: A7 J- k5 U, x; f! z
    6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y4 A7 G- v/ L- m: \5 c
    6 - 6 - Advanced Optimization (14 min).mkv
    0 j& z; x5 L( e6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z
    & r+ A0 W+ Y; [+ L# z7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z& L' s+ }( I* }5 ~
    7 - 2 - Cost Function (10 min).mkv) @1 g- e% n7 ?3 Q& f1 e0 q9 h+ h
    7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w
    ; Y+ R9 d" s" g- C# W7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A' z& a2 I% N  c8 |8 P! x. q
    8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e6 z$ D1 Q+ x4 V( h0 l7 ^" s
    8 - 2 - Neurons and the Brain (8 min).mkv+ H2 U5 H) S$ o, b
    8 - 3 - Model Representation I (12 min).mkv
    2 [; Z- _; ?9 w, k: ?$ T* Y8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H% j) [) [" s0 y
    8 - 5 - Examples and Intuitions I (7 min).mkv
    % k1 `1 |1 N3 h8 - 6 - Examples and Intuitions II (10 min).mkv$ x3 r$ m* Y! I4 ^; o  Y
    8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z
    # f" y3 f# d# ]# X0 s" T4 z9 - 1 - Cost Function (7 min).mkv
    1 a& }$ u/ R. d$ X8 z9 - 2 - Backpropagation Algorithm (12 min).mkv
    / r5 i6 T; k) y4 p; v1 o" p- n2 k8 G9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @
    7 [3 c: Z9 O! C3 w% ^8 V9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L, T7 K/ J) G! C2 {6 s0 j) u
    9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U6 Z" p3 Y2 Z5 J6 S5 \5 {+ x; ^6 g
    9 - 6 - Random Initialization (7 min).mkv; @. P& w2 l0 V  d
    9 - 7 - Putting It Together (14 min).mkv0 N9 A+ p3 ?0 M2 g7 F& R/ h
    9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E
    5 Y1 L3 u, S4 ^! h( h9 H10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e3 t  {9 W& k2 i6 t2 X
    10 - 2 - Evaluating a Hypothesis (8 min).mkv
    6 D% h# G2 a% [: o% ]* R1 }10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv
    6 z: f+ G4 j/ d# c: D) C10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G0 M" {; l1 J5 }7 m4 K9 G& }" D/ P1 ~5 z
    10 - 5 - Regularization and Bias_Variance (11 min).mkv* J3 W' C! O( r
    10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M
    . h5 n& I5 C- x10 - 7 - Deciding What to Do Next Revisited (7 min).mkv: p4 |+ y' p5 q2 g/ D
    11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m
    + }$ K& w4 m. {5 o- u11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v+ _- x, n3 ~4 @" i: U  {
    11 - 3 - Error Metrics for Skewed Classes (12 min).mkv7 U# H/ E) Y+ }& V3 D
    11 - 4 - Trading Off Precision and Recall (14 min).mkv
    ! t( r. W4 c4 }: W. p11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a; l/ X* o( w& P5 ]9 \7 j
    12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c, P& I0 A5 |3 {
    12 - 2 - Large Margin Intuition (11 min).mkv! s2 U8 Y2 |; f( |
    12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv
    2 ^; {' x9 e' A4 L12 - 4 - Kernels I (16 min).mkv
    * _# v6 q6 H8 V! u! ~4 j12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M
    ! E9 e/ M/ g3 x, G/ i/ G12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j9 r3 g) w5 H! Q7 u8 c
    13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d
    9 N2 B, h7 S0 K# h13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k
    $ W3 ~9 s( `/ A! h# l1 d13 - 3 - Optimization Objective (7 min)(1).mkv+ g8 @3 I2 s+ A$ ~+ q( ~' G+ q
    13 - 3 - Optimization Objective (7 min).mkv
    : k1 Q5 X% Z8 C13 - 4 - Random Initialization (8 min).mkv/ s9 j0 d" W3 M4 e
    13 - 5 - Choosing the Number of Clusters (8 min).mkv
      X3 `8 |7 d* a. N4 u; L14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q# |2 j3 h* c+ n+ Q8 ]% ?4 y
    14 - 2 - Motivation II_ Visualization (6 min).mkv7 k: i7 g/ j# u1 y, P& V
    14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv  i* _" H0 k0 s3 f6 \. B. t) [8 |
    14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [4 l) ^7 K, U9 V  G# ~+ S
    14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g
    " a/ ]$ ]6 d- A8 Y# W1 j14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @
    ! e" J& n; {9 _6 v14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W
    / D: P( b8 ^/ n% L15 - 1 - Problem Motivation (8 min).mkv
    ( g1 \- J& u% o. [" t7 a( S' [' s15 - 2 - Gaussian Distribution (10 min).mkv( ]& b  B9 ~; [: y) `. K1 k/ n# u+ [
    15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A, S, A2 U; L% e7 p! w
    15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
    0 o2 b1 y' s6 A. k15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv: M& P' x! ^% G
    15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q
    0 p" k- A. y9 h8 w+ I15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U/ O  n9 a* l6 U  n- N' x/ g
    15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e$ `( v7 Y! V2 j4 o' V, O/ z1 j
    16 - 1 - Problem Formulation (8 min).mkv
    3 K. L5 E3 w' k8 z16 - 2 - Content Based Recommendations (15 min).mkv( R* i7 H* W7 k
    16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e2 q9 Z, J! b& `% k
    16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]
    - e9 \3 ?; j, X5 Q8 B16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv; U6 I( x( e1 V* X/ w: C3 `0 U4 _' v
    16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv
    ; P8 m  N$ j6 E1 U( Z* Y! `# t17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R
    5 `/ m5 y7 \$ P+ X17 - 2 - Stochastic Gradient Descent (13 min).mkv
    2 _2 A! _1 a$ [$ A' I& b( z17 - 3 - Mini-Batch Gradient Descent (6 min).mkv3 U6 ]+ O3 G0 d2 e. H* B
    17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f
    6 F" c  L5 O$ r' U# ~5 k1 ~17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?" c9 V8 Z7 k, h( E2 F, s
    17 - 6 - Map Reduce and Data Parallelism (14 min).mkv, P, j, u" R& R* n
    18 - 1 - Problem Description and Pipeline (7 min).mkv
    0 p1 `' c; U% ^18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d
    $ X  m4 @1 P7 I$ K18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L* ^; X, h/ n9 N/ d/ V5 [
    18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv- }% f* |. [# `
    19 - 1 - Summary and Thank You (5 min).mkv/ [! k7 z3 m# Q3 M: r* v" l7 n% O0 T) y% F0 }/ v
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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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