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Deep Learning
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Intuition behind concepts. Research paper summaries and discussion. Links to tools, datasets, code and other resources.
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A Non-Technical Introduction to Machine Learning
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discussion
Variational Autoencoders
Variational autoencoders were introduced to efficiently to solve the inference problem in deep generative models. A generative model generates data by sampling as follows: z ~ p(z), x ~ p(x|z). p in this case can be modeled using deep neural networks. But using deep neural networks makes it difficult to infer z, given an x. This paper introduces a novel method based on calculus of variations to approximately infer z given x. The authors start with an intuitive objective to optimize, reduce it to the problem of optimization of a variational lower bound and finally illustrate an example using deep neural networks to model the probability distributions. In contrast to GANs (which can easily diverge even with small variations), variational autoencoders are easier to train. Similar to GANs, trained variational autoencoders have be used to generate new images and interpolate between images.
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tutorial
Perceptron algorithm
Lets say you have a dataset for binary classification. Each data point x has n-dimensions, and the target value is y = 1 or -1 for the two classes.
The perceptron
The perceptron algorithm can be visualized as follows.
For a given data point x, the prediction is simply b...
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The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)
Introduction
In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and co...
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