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…

## Introduction to Autoencoders

All neural networks architectures lay on the same principles. There are neurons with biases and activation functions connected with weighted connections. However, different problems require the different mix of those neurons and connections. That is how we ended up with…

## 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…

## 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…

## 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…

## Implementing Self-Organizing Maps with .NET Core

The code that accompanies this article can be downloaded here. In the previous articles, we explored what Self-Organizing Maps are and how you can implement them using Python and TensorFlow. One of the most interesting things about these networks is that…

## Introduction to Self-Organizing Maps

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…

## Two Ways to Implement LSTM Network using Python – with TensorFlow and Keras

In the previous article, we talked about the way that powerful type of Recurrent Neural Networks - Long Short-Term Memory (LSTM) Networks function. Now, we will see how to implement this kind of networks.