**Variational Autoencoders Explained in Detail** - Nov 30, 2018.

We explain how to implement VAE - including simple to understand tensorflow code using MNIST and a cool trick of how you can generate an image of a digit conditioned on the digit.

Tags: Autoencoder, Deep Learning, Machine Learning, MNIST, TensorFlow

**Building a Basic Keras Neural Network Sequential Model** - Jun 29, 2018.

The approach basically coincides with Chollet's Keras 4 step workflow, which he outlines in his book "Deep Learning with Python," using the MNIST dataset, and the model built is a Sequential network of Dense layers. A building block for additional posts.

Tags: Keras, MNIST, Neural Networks, Python

**Using Topological Data Analysis to Understand the Behavior of Convolutional Neural Networks** - Jun 28, 2018.

Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way.

Tags: Ayasdi, Convolutional Neural Networks, MNIST, Neural Networks, Topological Data Analysis

**Artificial Neural Networks (ANN) Introduction, Part 1** - Dec 8, 2016.

This intro to ANNs will look at how we can train an algorithm to recognize images of handwritten digits. We will be using the images from the famous MNIST (Mixed National Institute of Standards and Technology) database.

Tags: Algobeans, Image Recognition, MNIST, Neural Networks

**New sequence learning data set** - Sep 17, 2016.

A new data set for the study of sequence learning algorithms is available as of today. The data set consists of pen stroke sequences that represent handwritten digits, and was created based on the MNIST handwritten digit data set.

Tags: GitHub, Image Recognition, Machine Learning, MNIST, Sequences

**MNIST Generative Adversarial Model in Keras** - Jul 19, 2016.

This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.

Tags: Adversarial, Generative Models, MNIST

**Implementing Neural Networks in Javascript** - May 12, 2016.

Javascript is one of the most prevalent and fastest growing languages in existence today. Get a quick introduction to implementing neural networks in the language, and direction on where to go from here.

Tags: Javascript, MNIST, Neural Networks