Back
Tags: #neural networks

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 ...

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 ...

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 ...

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 ...

Implementing Minibatch Gradient Descent for Neural Networks
Let's use Python and Numpy to implement Minibatch Gradient Descent algorithm for a simple 3layers Neural Networks.