This major idea, first presented by Ian Goodfellow from the University of Montreal back in 2014, is still regarded as one of the biggest breakthroughs in the field. Facebook’s AI research director Yann LeCun called this concept “the most interesting idea in the last 10 years in Machine Learning”.

## Introduction to Generative Adversarial Networks (GANs)

Deep Learning zoo is getting bigger by the day. This is probably due to the fact that we are "crossing the chasm" with this technology and that we are entering "early majority" phase. Simply put, people find more and more ways…

## 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…

## Generate Music Using TensorFlow and Python

Not many people know, but apart from doing all techy stuff, I sometimes make music. I even have few EP releases and one album. They are terrible, but hey, it is something to pass the time. Anyhow, after getting deeper…

## 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…

## Implementing Restricted Boltzmann Machine with Python and TensorFlow

In one of the previous articles, we started learning about Restricted Boltzmann Machine. It was quite a journey since we first had to figure out what energy-based models are, and then to find out how a standard Boltzmann Machine functions.…

## 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…

## Introduction to Restricted Boltzmann Machines

So far in our artificial neural network journey, we have explored typical statistical models. In general, the entire point of statistical modeling and machine learning is to detect dependencies and connections between input variables. Standard Neural networks are exceptional at…

## Self- Organizing Maps Series

So far in our artificial neural network series, we have covered only neural networks that are using supervised learning. To be more precise, we only explored neural networks that have input and output data available to them during the learning process. Based…

## Credit Card Fraud Detection Using Self-Organizing Maps and Python

In the previous three articles, we explored the world of Self-Organizing Maps. First, we got some theoretical background on the subject. Then in the second article, we saw how we could implement Self-Organizing Maps using TensorFlow. After that, in the…