NIPS 2017 - presentations from the Deep Learning session

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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
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CS224D Lecture 11 - 3rd May 2016 Deep Learning
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Lecture 1-1. Why do we need Machine Learning? Deep Learning
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Lecture 1-2. What are Neural Networks? Deep Learning
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Lecture 1-3. Some simple models of neurons Deep Learning
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Lecture 1-4. A simple example of learning Deep Learning
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Lecture 1-5. Three types of learning Deep Learning
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Lecture 2-1. Types of neural network architectures Deep Learning
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Lecture 2-2. Perceptions: The first generation of neural networks Deep Learning
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Lecture 2-3. A geometrical view of perceptrons Deep Learning
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Lecture 2-4. Why the learning works Deep Learning
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Lecture 2-5. Whar perceptrons can't do Deep Learning
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Lecture 3-1. Learning the weights of a linear neuron Deep Learning
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Lecture 3-2. The error surface for a linear neuron Deep Learning
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Lecture 3-3. Learning the weights of a logistic output neuron Deep Learning
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Lecture 3-4. The backpropagation algorithm Deep Learning
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Lecture 3-5. Using the derivatives computed by backpropagation Deep Learning
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Lecture 4-1. Learning to predict the next word Deep Learning
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Lecture 4-2. A brief diversion into cognitive science Deep Learning
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Lecture 4-3. Another diversion: The softmax output function Deep Learning

NIPS 2017

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