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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.
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 ...
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 ...
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).
Conditional Generative Adversarial Nets in TensorFlow
Having seen GAN, VAE, and CVAE model, it is only proper to study the Conditional GAN model next!
Residual Net
In this post, we will look into the record breaking convnet model of 2015: the Residual Net (ResNet).
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 ...
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 ...
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. ...
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 ...