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,资源教程下载

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课程名称

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,资源教程下载

课程目录

00_Neural Networks for Machine Learning

00_Neural Networks for Machine Learning

hinton-ml

1.Why do we need machine learning.mp4

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

11.Learning the weights of a linear neuron [12 min].srt

12.The error surface for a linear neuron [5 min].mp4

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

13.Learning the weights of a logistic output neuron [4 min].mp4

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].srt

15.Using the derivatives computed by backpropagation [10 min].mp4

15.Using the derivatives computed by backpropagation [10 min].srt

16.Learning to predict the next word [13 min].mp4 

16.Learning to predict the next word [13 min].srt

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].mp4

19.Neuro-probabilistic language models [8 min].srt

2.What are neural networks1 R0 

2.What are neural networks.mp4

20.Ways to deal with the large number of possible outputs [15 min].mp4

20.Ways to deal with the large number of possible outputs [15 min].srt

21.Why object recognition is difficult [5 min].mp4

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

22.Achieving viewpoint invariance [6 min].mp4

22.Achieving viewpoint invariance [6 min].srt

23.Convolutional nets for digit recognition [16 min].mp4

23.Convolutional nets for digit recognition [16 min].srt

24.Convolutional nets for object recognition [17min].mp4

24.Convolutional nets for object recognition [17min].srt

25.Overview of mini-batch gradient descent.mp4

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.srt

27.The momentum method.mp4

27.The momentum method.srt

28.Adaptive learning rates for each connection.mp4

28.Adaptive learning rates for each connection.srt

3.Some simple models of neurons [8 min].mp4

3.Some simple models of neurons [8 min].srt

31.Training RNNs with back propagation.mp4

31.Training RNNs with back propagation.srt

32.A toy example of training an RNN.mp4

32.A toy example of training an RNN.srt

33.Why it is difficult to train an RNN.mp4

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].mp4

38.Echo State Networks [9 min].srt

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

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

41.Using noise as a regularizer [7 min].srt

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

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

46.Mixtures of Experts [13 min].mp4

46.Mixtures of Experts [13 min].srt

47.The idea of full Bayesian learning [7 min].mp4

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

49.Dropout [9 min].srt

5.Three types of learning [8 min].mp4

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

50.Hopfield Nets [13 min].mp4

50.Hopfield Nets [13 min].srt

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

51.Dealing with spurious minima [11 min].srt

52.Hopfield nets with hidden units [10 min].mp4

52.Hopfield nets with hidden units [10 min].srt

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

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

55.Boltzmann machine learning [12 min].mp4

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].mp4

58.An example of RBM learning [7 mins].srt

59.RBMs for collaborative filtering [8 mins].mp4

59.RBMs for collaborative filtering [8 mins].srt

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

61.Belief Nets [13 min].srt

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

62.Learning sigmoid belief nets [12 min].srt

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

64.Learning layers of features by stacking RBMs [17 min].srt

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 

66.What happens during discriminative fine-tuning

67.Modeling real-valued data with an RBM [10 mins].mp4

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

69.From PCA to autoencoders [5 mins].mp4

69.From PCA to autoencoders [5 mins].srt

70.Deep auto encoders [4 mins].mp4

70.Deep auto encoders [4 mins].srt

71.Deep auto encoders for document retrieval [8 mins].mp4

71.Deep auto encoders for document retrieval [8 mins].srt

72.Semantic Hashing [9 mins].mp4

72.Semantic Hashing [9 mins].srt

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

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

74.Shallow autoencoders for pre-training [7 mins].mp4 

74.Shallow autoencoders for pre-training [7 mins].srt

8.A geometrical view of perceptrons [6 min].mp4

8.A geometrical view of perceptrons [6 min].srt

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

9.Why the learning works [5 min].srt

neuralnets-2012-001

01_Lecture16

01_Why_do_we_need_machine_learning_13_min.mp4

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