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…

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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 “Implementing Restricted Boltzmann Machine with Python and TensorFlow”

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.…

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…

3 comments on “Introduction to Restricted Boltzmann Machines”

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…