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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