Lecture 14-5. (Optional) RBMs are infinite sigmoid belief nets

  • 2 views
  • 17 minutes, 11 seconds
  • Uploaded by Deep Learning

Related videos

64:32
Свёрточные сети и их приложения к компьютерному зрению Deep Learning
66:18
GPU и глубокое обучение Deep Learning
96:56
ЕМ-алгоритм и его обобщения в задачах обучения с неполной информацией Deep Learning
50:37
Введение в проблематику построения систем анализа больших данных – мировых научных публикаций и патентов Deep Learning
90:06
Deep Learning: a Revolution in Artificial Intelligence Deep Learning
101:30
Why Deep Learning? Deep Learning
62:49
Multilayered Networks and Gradient-based Backpropagation Deep Learning
64:22
Deep Learning in Practice Deep Learning
58:57
Convolutional Neural Networks Deep Learning
24:07
Convolutional Networks and their Applications in Vision Deep Learning
54:19
Recurrent Neural Networks and their Applications in Natural Language Processing Deep Learning
61:01
Reasoning, Attention, Memory Deep Learning
106:55
Unsupervised Learning Deep Learning
45:57
The Mathematical Mysteries behind Convolutional Neural Networks Deep Learning
70:35
Optimizing and Training Recurrent Networks Deep Learning
47:54
Speech Recognition Deep Learning
85:32
Metric Learning for Structured Predictions Deep Learning
65:03
Translating and Natural Language Processing Deep Learning
61:10
Deep Learning and Reasoning, Memory-Augmented Networks Deep Learning
75:17
CS224D Lecture 1 - 29th Mar 2016 Deep Learning
78:13
CS224D Lecture 2 - 31st Mar 2016 Deep Learning
77:56
CS224D Lecture 3 - 5th Apr 2016 Deep Learning
77:42
CS224D Lecture 4 - 7th Apr 2016 Deep Learning
79:06
CS224D Lecture 5 - 12th Apr 2016 Deep Learning
78:28
CS224D Lecture 6 - 14th Apr 2016 Deep Learning
79:46
CS224D Lecture 7 - Introduction to TensorFlow (19th Apr 2016 Deep Learning
81:10
CS224D Lecture 8 - 21st Apr 2016 Deep Learning
78:44
CS224D Lecture 10 - 28th Apr 2016 Deep Learning
81:12
CS224D Lecture 11 - 3rd May 2016 Deep Learning
9:31
AWS Tutorial Deep Learning
13:14
Lecture 1-1. Why do we need Machine Learning? Deep Learning
8:30
Lecture 1-2. What are Neural Networks? Deep Learning
8:23
Lecture 1-3. Some simple models of neurons Deep Learning
5:38
Lecture 1-4. A simple example of learning Deep Learning
7:37
Lecture 1-5. Three types of learning Deep Learning
7:28
Lecture 2-1. Types of neural network architectures Deep Learning
8:16
Lecture 2-2. Perceptions: The first generation of neural networks Deep Learning
6:24
Lecture 2-3. A geometrical view of perceptrons Deep Learning
5:09
Lecture 2-4. Why the learning works Deep Learning
14:34
Lecture 2-5. Whar perceptrons can't do Deep Learning
11:54
Lecture 3-1. Learning the weights of a linear neuron Deep Learning
5:02
Lecture 3-2. The error surface for a linear neuron Deep Learning
3:56
Lecture 3-3. Learning the weights of a logistic output neuron Deep Learning
11:50
Lecture 3-4. The backpropagation algorithm Deep Learning
9:49
Lecture 3-5. Using the derivatives computed by backpropagation Deep Learning
12:33
Lecture 4-1. Learning to predict the next word Deep Learning
4:26
Lecture 4-2. A brief diversion into cognitive science Deep Learning
7:20
Lecture 4-3. Another diversion: The softmax output function Deep Learning

Neural Networks for Machine Learning (Coursera)

You might be interested

egolove pee слив порно
в молоке
Порно SFM Жена Изменяет.Муж В Командировке,А Жена Трахается С Соседом Во Все Дыры.Реа
epic crossover русский
天天精品人人综合天堂
一坐脸