Automatic Differentiation Explained

Introduction There are several methods to calculate gradients in computer programs: (1) Manual differentiation; (2) Symbolic differentiation; (3) Finite differences […]

Improvements in Deep Q Learning: Dueling Double DQN, Prioritized Experience Replay, and fixed Q-targets

Deep Q-Learning was introduced in 2014. Since then, a lot of improvements have been made. So, today we’ll see four […]

A review on Deep learning based recommendation systems

Source: https://jameskle.com/writes/rec-sys-part-2 INTRODUCTION The number of research publications on deep learning-based recommendation systems has increased exponentially in the past recent […]

Four steps to setup Pytorch for my laptop GPU NVIDIA GTX 960m (Asus VivoBook n552vw) in Ubuntu 16.4

In this post, I’m gonna describe the steps I used to make Pytorch use GPU on my laptop (It takes […]

Restricted Boltzmann Machines (RBMs) Simply Explained

Contents Definition & Structure Reconstructions Probability Distributions Code Sample: Stacked RBMS Parameters & k Continuous RBMs Next Steps Other Resources […]

Transfer Learning in Convolutional Neural Networks simply explained

Transfer learning involves taking a pre-trained neural network and adapting the neural network to a new, different data set. Depending […]

Gated Recurrent Unit (GRU) With PyTorch

https://blog.floydhub.com/gru-with-pytorch/ Have you heard of GRUs? The Gated Recurrent Unit (GRU) is the younger sibling of the more popular Long Short-Term […]

Long Short-Term Memory Networks with PyTorch

LSTMs are a particular variant of RNNs, therefore having a grasp of the concepts surrounding RNNs will significantly aid your […]