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Tags: #neural networks
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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 ...
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Implementing BatchNorm in Neural Net
BatchNorm is a relatively new technique for training neural net. It gaves us a lot of relaxation when initializing the ...
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Implementing Dropout in Neural Net
Dropout is one simple way to regularize a neural net model. This is one of the recent advancements in Deep ...
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Beyond SGD: Gradient Descent with Momentum and Adaptive Learning Rate
There are many attempts to improve Gradient Descent: some add momentum, some add adaptive learning rate. Let's see what's out ...
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Implementing Minibatch Gradient Descent for Neural Networks
Let's use Python and Numpy to implement Minibatch Gradient Descent algorithm for a simple 3-layers Neural Networks.