Adventures in AI part 1: What is a gradient descent algorithm?

When you start out with machine learning and AI you will learn very quickly that there's a lot of math involved. All this math is very hard to understand, especially if you have a background in software engineering rather than statistics. Most of the stuff you will find on the internet assumes that you know your statistics, which you probably don't.

In this series I will invite you along on my personal AI trip along some very cool algorithms and seriously hard topics. I will explain them as simple as I can so you too can start to use machine learning and deep learning in your daily work. <!--more-->

Monitor progress of your Keras based neural network using Tensorboard

In the past few weeks I've been breaking my brain over a way to automatically answer questions using a neural network. I have a working version, but debugging a neural network is a nightmare.

Neural networks by their very nature are hard to reason about. You can't really find out how or why something happened in a neural network, because they are too complex for that. Also, there's a real art to selecting the right number of layers, the right number of neurons per layers and which optimizer you should use.