Bringing clarity to the crypto-asset class

Since the birth of transistors, we’ve seen an explosive growth in software products. Whatever the use case, code is a means of automation that ultimately increases our productivity. The blockchain is simply an extension of this pervasive computing trend, aimed at the automation of trust.

It started with Bitcoin. The real breakthrough lay in the utilisation of cryptography to create digital scarcity. Through a carefully defined mathematical model, Satoshi Nakamoto achieved a fully predictable issuance model for a currency. This eliminates the need for a central authority that could interrupt with the transactions or value.

In many developed economies, we…

This second and final part looks at applying machine learning techniques to the Inside Airbnb data set. More precisely, we want to predict as accurately as possible the price of a new listing given its attributes.

As with most machine learning projects, the majority of the time is spent understanding and preparing the data-set into a format the machine can work with. In the previous article, we had a very brief look at the data. As you can see below, there’s a total of 95 features for 15,181 samples (listings).

All features in 2017 Inside Airbnb Data-set

Given the fact that there are so many features, it’s…

This article explores Airbnb data and is broken down into two sections:

  • Part 1 explores the data and looks for correlations with house prices
  • Part 2 explores machine learning techniques to predict future prices

The gig economy is profoundly reshaping how we consume products and services. Unlike previously thought, we are not seeing a decline in employment within the same industry. Instead, as observed by HBR, there is actually an increase of hired employees. This correlation can be seen across the transport industry with the rise of Uber and also in the accommodation industry, after the emergence of Airbnb. …

PHEET — an innovative solution for custom foot orthotics

This article was originally written for 3DMedNet

I believe we are now a point of convergence where additive manufacturing (also known as 3D printing) is becoming a cost effective solution to deliver mass customized products. The nature of this technology lacks economy of scale but allows for a high level of customization making it an ideal mean of production to respond to anatomical variances on an individual basis.

With the underlying knowledge of this technology I was listening to my brother speak about his seemingly endless search for a solution to cure his foot problem which kept him away from…

Due to the severe subprime mortgage crisis I have some understanding for the scepticism surrounding the stock market. However, in a time of increasing national debt and aging population it is more important than ever for people to take control of their finances as early as possible. Thanks to the magic of compounding an early start can make a drastic difference.

To give you an idea on what your end goal should be let me introduce you to the concept of Safe Withdrawal Rate (SWR) which stands at 4%. To get an idea of how much you need to retire…

Part 1, Part 2 & Part 3

In this last section we’ll step away from simulating how the brain works to implement back propagation which bears no resemblance to the biological function of the brain.

We’re essentially going to use Newton’s method of optimisation to follow a gradient descent down towards the right answer. This is done by differentiating the answer you currently have:

We want to iterate through each neuron and adjust the weights so that the result you would achieve is closer to the desired result.

Let’s look at an example, in the case of card #1825 which is a 3 the ideal output layer…

Part 1, Part 2 & Part 3

Now we’ve got our initial setup we can get onto the more interesting stuff. The next step is to complete the network by adding a hidden layer and the output layer. We will then construct the forward feeding structure that we discussed in part 1.

Firstly let’s expand on our “Neuron” class so we can initialise it in two ways, either as an input layer with no inputs other than the card it gets shown. Or as hidden or output layer which receives as input the output of the preceding layer. We also give is a some weights which are…

Part 1, Part 2 & Part 3

There’s something particularly interesting about a computer learning to do simple task, with it comes a given probability that it might one day surpass human capabilities.

This tutorial aims to teach creative coders how to create an Artificial Neural Network (ANN). To be a little more specific our network will consist of neurons and perceptrons which will feed-forward and use backpropagation to learn. This may sound alien to you but fear not, I will explain everything in detail. It is also ANN in its simplest form, therefore, please don’t expect a Tensorflow challenger, its beauty lies in its simplicity. …

Charles Fried

CTO / Co-Founder -> & | Curious Technologist |

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