It’s been a while we haven’t covered any machine learning algorithm. Last time we discussed the Markov Decision Process (or MDP). Today we’re going to build our knowledge on top of the MDP and see how we can generalise our MDP to solve more complex problems. Reinforcement learning really hit the news back in 2013 […]

# Category archives: Machine learning

## Markov Decision Process

Now that we know about Markov chain, let’s focus on a slightly different process: the Markov Decision Process. This process is quite similar to a Markov chain but adds more concept into it: Actions and Rewards. Having a reward means that it’s possible to learn which action yield the best rewards. This type of learning […]

## Hidden Markov Model

Last time we talk about what is a Markov chain. However there is one big limitation: A Markov chain implies that we can directly observe the state of the process. (e.g the number of people in the queue). Many times we can only access an indirect representation or noisy measure of the state of the […]

## Markov chain

I’ve heard the term “Markov chain” a couple of times but never had a look at what it actually is. Probably because it sounded too impressive and complicated. It turns out it’s not as bad as it sounds. In fact the whole idea is pretty intuitive and once you get a feeling of how things […]

## Distributed training of neural networks

Distributed training of neural networks is something I’ve always wanted to try but couldn’t find much information about it. It seems most people train their models on a single machine. In fact it makes sense because training on a single machine is much more efficient than distributed training. Distributed training incurs additional cost and is […]

## DeepLearning4J introduction

Following my previous post on ND4J I think it’s time for a proper introduction to deepLearning4J (a.k.a. DL4J). In this post I’ll try to do something similar to the tensorflow introduction. That is installing DL4J, get our hands on it and then build a very basic neural network.

## Nd4j – Numpy for the JVM

I have spent years programming in Java and one thing (among others) that I found frustrating is the lack of mathematical libraries (not to say Machine learning framework) on the JVM. In fact if you’re a little interested in machine learning you’ll notice that all the cool stuffs are written in C++ (for performance reasons) […]

## TF-IDF

The idea from this blog post came after finishing the lab on TF-IDF of the edx Spark specialisation courses. In this course the labs follow a step-by-step approach where you need to write some lines of code at every step. The lab is very detailed and easy to follow. However I found that focusing on […]

## Neural network hardware considerations

This post will present some principles to be considered when choosing the hardware that will run some neural net computation, either for training models or making prediction using an existing model (i.e. inference). Need more cores Usually CPU are considered to not perform well enough when it comes to neural network computing and they are […]

## Hyper parameters tuning cheat sheet

After the rather long post on how to implement a neural network, here is a brief summary on how each hyper-parameter affects the network.