0 comments on “Introduction to Adversarial Autoencoders”

Introduction to Adversarial Autoencoders

Generative Adversarial Networks (GAN) shook up the deep learning world. When they first appeared in 2014, they proposed a new and fresh approach to modeling and gave a possibility for new neural network architectures to emerge. Since standard GAN architecture is composed of two neural networks, we can play around and use different approaches for those networks and thus create new and shiny architectures, like Adversarial Autoencoder.

0 comments on “3 Ways to Implement Autoencoders with TensorFlow and Python”

3 Ways to Implement Autoencoders with TensorFlow and Python

In one of the previous articles, we started our journey into the world of Autoencoders. We saw that they are one special kind of neural networks, that was able to utilize techniques of supervised learning for unsupervised learning. One might…

0 comments on “Stock Price Prediction Using Hidden Markov Model”

Stock Price Prediction Using Hidden Markov Model

Learn to predict stock prices using HMM in this article by Ankur Ankan, an open source enthusiast, and Abinash Panda, a data scientist who has worked at multiple start-ups. A Hidden Markov Model (HMM) is a specific case of the state space…

0 comments on “Machine Learning to Catch Up with Human Intelligence – The Outcome”

Machine Learning to Catch Up with Human Intelligence – The Outcome

With our dependence on technology only becoming stronger and with some recent privacy-destroyed scandals making people worry, robots became the talk of the town once again. Almost every film, TV show, and video game tries to raise a question about…

2 comments on “Implementing Restricted Boltzmann Machine with .NET Core”

Implementing Restricted Boltzmann Machine with .NET Core

The code that accompanies this article can be downloaded here. In the previous article, we had a chance to see what is the Restricted Boltzmann Machine and how it functions and learns. The path was bumpy because first, we needed to…