[人工智能] 684个视频教程 国外关于深度学习的新版视频教程 全英文+英文字幕

课程介绍:

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都是一些关于大数据深度学习的视频教程,国外教授录制,带英文字幕.& L6 \7 g8 K: w* `) P, D



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详细目录:



├─00_Neural Networks for Machine Learning

│  └─00_Neural Networks for Machine Learning

│      ├─hinton-ml* U+ J9 y- X- t  e( u

│      │      1.Why do we need machine learning* @8 |  ~* M' j' ]3 P' A

│      │      1.Why do we need machine learning.mp4

│      │      10.What perceptrons can't do [15 min].mp4

│      │      10.What perceptrons can't do [15 min].srt

│      │      11.Learning the weights of a linear neuron [12 min].mp4" p  v0 A# c& r( }- w$ \

│      │      11.Learning the weights of a linear neuron [12 min].srt3 V% K8 W6 A6 {! [. R5 I9 h6 v) R

│      │      12.The error surface for a linear neuron [5 min].mp43 W7 G/ L2 A6 C: A- K2 N7 }

│      │      12.The error surface for a linear neuron [5 min].srt

│      │      13.Learning the weights of a logistic output neuron [4 min].mp4! F) a) k( u/ m4 x

│      │      13.Learning the weights of a logistic output neuron [4 min].srt

│      │      14.The backpropagation algorithm [12 min].mp4

│      │      14.The backpropagation algorithm [12 min].srt8 i! V% y( v! ?) k0 Z5 @4 I) ?

│      │      15.Using the derivatives computed by backpropagation [10 min].mp4, p- X6 N' m; a% }! W" \* B

│      │      15.Using the derivatives computed by backpropagation [10 min].srt3 ]. x: R; e8 ^) j

│      │      16.Learning to predict the next word [13 min].mp4  L8 G% _+ Z( m- w5 I" `

│      │      16.Learning to predict the next word [13 min].srt$ x" D% O0 p! c+ ?; Q  ~- W- ^" C

│      │      17.A brief diversion into cognitive science [4 min].mp4

│      │      17.A brief diversion into cognitive science [4 min].srt

│      │      19.Neuro-probabilistic language models [8 min].mp48 d( ^. a% e* T# f* O

│      │      19.Neuro-probabilistic language models [8 min].srt/ m( _* @* Z) }

│      │      2.What are neural networks1 R0 n6 ]; a4 O/ c! C

│      │      2.What are neural networks.mp4

│      │      20.Ways to deal with the large number of possible outputs [15 min].mp45 `' Y6 l* L( u3 u

│      │      20.Ways to deal with the large number of possible outputs [15 min].srt! q9 X! l* N( x

│      │      21.Why object recognition is difficult [5 min].mp48 B6 s; j7 i4 G- Z8 O; B

│      │      21.Why object recognition is difficult [5 min].srt

│      │      22.Achieving viewpoint invariance [6 min].mp4

│      │      22.Achieving viewpoint invariance [6 min].srt3 n4 k+ h& C. f. O

│      │      23.Convolutional nets for digit recognition [16 min].mp4' n" G$ A2 o4 Q3 H0 N

│      │      23.Convolutional nets for digit recognition [16 min].srt# k, U3 F" H* Q6 V7 l9 ?8 o6 B

│      │      24.Convolutional nets for object recognition [17min].mp44 X# `# Y# S) i. Q: q* h

│      │      24.Convolutional nets for object recognition [17min].srt7 i( {, v- z6 w' b" z) ]4 d& u

│      │      25.Overview of mini-batch gradient descent.mp4/ w! O1 I4 r5 G; l) S" c, w* M1 S

│      │      25.Overview of mini-batch gradient descent.srt

│      │      26.A bag of tricks for mini-batch gradient descent.mp4

│      │      26.A bag of tricks for mini-batch gradient descent.srt6 T/ i( s1 w' }% J: I

│      │      27.The momentum method.mp4: s+ Y" x8 z! ?1 l, v

│      │      27.The momentum method.srt: I4 ~! P4 x2 p- t5 n

│      │      28.Adaptive learning rates for each connection.mp4

│      │      28.Adaptive learning rates for each connection.srt

│      │      3.Some simple models of neurons [8 min].mp47 P7 R& x3 s, ]7 e

│      │      3.Some simple models of neurons [8 min].srt, D4 t; \2 u5 v; }: c4 T

│      │      31.Training RNNs with back propagation.mp4) |$ Q+ g$ m$ R5 S

│      │      31.Training RNNs with back propagation.srt0 @2 z- h$ y) [/ R4 a

│      │      32.A toy example of training an RNN.mp4$ k- R! e) N& D+ d1 ]* M$ v" m. ?

│      │      32.A toy example of training an RNN.srt

│      │      33.Why it is difficult to train an RNN.mp43 R) \0 n$ l) t0 |) k5 S& m

│      │      33.Why it is difficult to train an RNN.srt

│      │      34.Long-term Short-term-memory.mp4

│      │      34.Long-term Short-term-memory.srt

│      │      35.A brief overview of Hessian Free optimization.mp4

│      │      35.A brief overview of Hessian Free optimization.srt

│      │      37.Learning to predict the next character using HF [12  mins].mp4

│      │      37.Learning to predict the next character using HF [12  mins].srt

│      │      38.Echo State Networks [9 min].mp43 B7 R* ~: \3 V4 Q

│      │      38.Echo State Networks [9 min].srt7 ?( f7 v" Z/ z  o# i' e( E( b

│      │      39.Overview of ways to improve generalization [12 min].mp4

│      │      39.Overview of ways to improve generalization [12 min].srt

│      │      4.A simple example of learning [6 min].mp4

│      │      4.A simple example of learning [6 min].srt: V- V3 x9 A6 Z. ^

│      │      40.Limiting the size of the weights [6 min].mp4

│      │      40.Limiting the size of the weights [6 min].srt

│      │      41.Using noise as a regularizer [7 min].mp4% X8 ?* F) p  v$ f

│      │      41.Using noise as a regularizer [7 min].srt" n/ \3 A- f& P7 O

│      │      42.Introduction to the full Bayesian approach [12 min].mp4

│      │      42.Introduction to the full Bayesian approach [12 min].srt

│      │      43.The Bayesian interpretation of weight decay [11 min].mp4# \9 @5 d2 K2 s8 l2 z) E! d

│      │      43.The Bayesian interpretation of weight decay [11 min].srt

│      │      44.MacKay's quick and dirty method of setting weight costs [4 min].mp4

│      │      44.MacKay's quick and dirty method of setting weight costs [4 min].srt

│      │      45.Why it helps to combine models [13 min].mp4

│      │      45.Why it helps to combine models [13 min].srt: m5 U  p5 _! k" K

│      │      46.Mixtures of Experts [13 min].mp4

│      │      46.Mixtures of Experts [13 min].srt

│      │      47.The idea of full Bayesian learning [7 min].mp48 @1 l  X, E" E" c" \

│      │      47.The idea of full Bayesian learning [7 min].srt

│      │      48.Making full Bayesian learning practical [7 min].mp4

│      │      48.Making full Bayesian learning practical [7 min].srt

│      │      49.Dropout [9 min].mp4& j4 M! q( Q7 k& H4 }) C

│      │      49.Dropout [9 min].srt& _) U- g8 q, Q2 W% R

│      │      5.Three types of learning [8 min].mp4; n4 R# R  N2 v: o

│      │      5.Three types of learning [8 min].srt

│      │      50.Hopfield Nets [13 min].mp4* W; t0 H# K2 h, t) X& o

│      │      50.Hopfield Nets [13 min].srt

│      │      51.Dealing with spurious minima [11 min].mp4

│      │      51.Dealing with spurious minima [11 min].srt% R' [' D4 w0 b" p! E

│      │      52.Hopfield nets with hidden units [10 min].mp47 r& u% \- Z$ Q

│      │      52.Hopfield nets with hidden units [10 min].srt9 [7 _3 }. T! B9 H0 V: |5 c) T, D# B

│      │      53.Using stochastic units to improv search [11 min].mp4

│      │      53.Using stochastic units to improv search [11 min].srt

│      │      54.How a Boltzmann machine models data [12 min].mp4: n5 T7 m- L: i! n4 x& {, Z) s/ x

│      │      54.How a Boltzmann machine models data [12 min].srt

│      │      55.Boltzmann machine learning [12 min].mp4) `+ V/ I! v7 o: l. e

│      │      55.Boltzmann machine learning [12 min].srt

│      │      57.Restricted Boltzmann Machines [11 min].mp4

│      │      57.Restricted Boltzmann Machines [11 min].srt

│      │      58.An example of RBM learning [7 mins].mp49 P8 X) n4 T) {8 w1 O

│      │      58.An example of RBM learning [7 mins].srt! m  w& t2 p( G5 f6 J

│      │      59.RBMs for collaborative filtering [8 mins].mp4$ C7 S; |. q$ V. d! @2 j

│      │      59.RBMs for collaborative filtering [8 mins].srt. n0 @3 j' q: v- q2 Y& ~" X; E

│      │      6.Types of neural network architectures [7 min].mp4

│      │      6.Types of neural network architectures [7 min].srt

│      │      60.The ups and downs of back propagation [10 min].mp4

│      │      60.The ups and downs of back propagation [10 min].srt

│      │      61.Belief Nets [13 min].mp4. Z( R! t3 K( r. j4 Y% n- Q

│      │      61.Belief Nets [13 min].srt

│      │      62.Learning sigmoid belief nets [12 min].mp4

│      │      62.Learning sigmoid belief nets [12 min].srt" T' Y& F3 r' A# R) _6 f& n

│      │      63.The wake-sleep algorithm [13 min].mp4

│      │      63.The wake-sleep algorithm [13 min].srt

│      │      64.Learning layers of features by stacking RBMs [17 min].mp4) N0 L- U# H  X

│      │      64.Learning layers of features by stacking RBMs [17 min].srt0 z5 v0 f7 h7 e- _

│      │      65.Discriminative learning for DBNs [9 mins].mp4

│      │      65.Discriminative learning for DBNs [9 mins].srt

│      │      66(1).What happens during discriminative fine-tuning- t. z9 q& v' u+ v* h8 p

│      │      66.What happens during discriminative fine-tuning

│      │      67.Modeling real-valued data with an RBM [10 mins].mp4" U. n" Q" H5 W0 ]1 Y

│      │      67.Modeling real-valued data with an RBM [10 mins].srt

│      │      69.From PCA to autoencoders [5 mins].mp4; g, J. G+ c/ s* g

│      │      69.From PCA to autoencoders [5 mins].srt- t/ h- M6 d9 R. @! t" D

│      │      70.Deep auto encoders [4 mins].mp4( \  V. H' |; c$ y

│      │      70.Deep auto encoders [4 mins].srt$ K- U/ v& F& h' g8 m) I1 \3 A

│      │      71.Deep auto encoders for document retrieval [8 mins].mp48 n: f5 p) t5 ]+ c7 U. J3 u

│      │      71.Deep auto encoders for document retrieval [8 mins].srt! B7 t4 Y. X, M. W1 }; V

│      │      72.Semantic Hashing [9 mins].mp4

│      │      72.Semantic Hashing [9 mins].srt7 M8 `' J8 q, F; i0 J

│      │      73.Learning binary codes for image retrieval [9 mins].mp4

│      │      73.Learning binary codes for image retrieval [9 mins].srt2 A5 F+ {! l" O6 K

│      │      74.Shallow autoencoders for pre-training [7 mins].mp4- S6 U8 h; ~) x* ^: d

│      │      74.Shallow autoencoders for pre-training [7 mins].srt1 M. ?( K* {) [7 O# j8 x

│      │      8.A geometrical view of perceptrons [6 min].mp42 ~- O+ n& K6 Y/ a1 V

│      │      8.A geometrical view of perceptrons [6 min].srt% C9 e# A: k% {, f! Z- ~8 D

│      │      9.Why the learning works [5 min].mp4

│      │      9.Why the learning works [5 min].srt. H- J. N0 U/ k1 D6 G2 K

│      │      ( k. |+ K( K- w% ^5 V9 T: k

│      └─neuralnets-2012-001

│          ├─01_Lecture16 j! Q% ]1 P9 d- W" c

│          │      01_Why_do_we_need_machine_learning_13_min.mp4