NIPS 2017 - presentations from the Deep Learning, Applications session

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Свёрточные сети и их приложения к компьютерному зрению Deep Learning
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GPU и глубокое обучение Deep Learning
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ЕМ-алгоритм и его обобщения в задачах обучения с неполной информацией Deep Learning
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Deep Learning: a Revolution in Artificial Intelligence Deep Learning
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Why Deep Learning? Deep Learning
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Multilayered Networks and Gradient-based Backpropagation Deep Learning
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Deep Learning in Practice Deep Learning
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Convolutional Neural Networks Deep Learning
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Convolutional Networks and their Applications in Vision Deep Learning
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Recurrent Neural Networks and their Applications in Natural Language Processing Deep Learning
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Reasoning, Attention, Memory Deep Learning
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Unsupervised Learning Deep Learning
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The Mathematical Mysteries behind Convolutional Neural Networks Deep Learning
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Optimizing and Training Recurrent Networks Deep Learning
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Speech Recognition Deep Learning
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Metric Learning for Structured Predictions Deep Learning
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Translating and Natural Language Processing Deep Learning
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Deep Learning and Reasoning, Memory-Augmented Networks Deep Learning
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CS224D Lecture 1 - 29th Mar 2016 Deep Learning
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CS224D Lecture 2 - 31st Mar 2016 Deep Learning
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CS224D Lecture 3 - 5th Apr 2016 Deep Learning
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CS224D Lecture 4 - 7th Apr 2016 Deep Learning
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CS224D Lecture 5 - 12th Apr 2016 Deep Learning
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CS224D Lecture 6 - 14th Apr 2016 Deep Learning
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CS224D Lecture 7 - Introduction to TensorFlow (19th Apr 2016 Deep Learning
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CS224D Lecture 8 - 21st Apr 2016 Deep Learning
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CS224D Lecture 10 - 28th Apr 2016 Deep Learning
81:12
CS224D Lecture 11 - 3rd May 2016 Deep Learning
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AWS Tutorial Deep Learning
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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
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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

NIPS 2017

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