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Tags: #machine learning
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Variational Autoencoder (VAE) in Pytorch
With all of those bells and whistles surrounding Pytorch, let's implement Variational Autoencoder (VAE) using it.
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Generative Adversarial Networks (GAN) in Pytorch
Pytorch is a new Python Deep Learning library, derived from Torch. Contrary to Theano's and TensorFlow's symbolic operations, Pytorch uses ...
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Linear Regression: A Bayesian Point of View
You know the drill, apply mean squared error, then descend those gradients. But, what is the intuition of that process ...
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MLE vs MAP: the connection between Maximum Likelihood and Maximum A Posteriori Estimation
In this post, we will see what is the difference between Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP).
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Conditional Generative Adversarial Nets in TensorFlow
Having seen GAN, VAE, and CVAE model, it is only proper to study the Conditional GAN model next!
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Residual Net
In this post, we will look into the record breaking convnet model of 2015: the Residual Net (ResNet).
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Generative Adversarial Nets in TensorFlow
Let's try to implement Generative Adversarial Nets (GAN), first introduced by Goodfellow et al, 2014, with TensorFlow. We'll use MNIST ...
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Deriving LSTM Gradient for Backpropagation
Deriving neuralnet gradient is an absolutely great exercise to understand backpropagation and computational graph better. In this post we will ...
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Convnet: Implementing Maxpool Layer with Numpy
Another important building block in convnet is the pooling layer. Nowadays, the most widely used is the max pool layer. ...
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Convnet: Implementing Convolution Layer with Numpy
Convnet is dominating the world of computer vision right now. What make it special of course the convolution layer, hence ...