Jupyter Notebook Tutorial: The Definitive Guide

This tutorial explains how to install, run, and use Jupyter Notebooks for data science, including tips, best practices, and examples.


Investigating octopus camouflage with ICA and hierarchical clustering

How neuroscientists study this amazing skill with machine learning algorithms Cephalopods (octopus, squids, cuttlefish) are natural masters in the technique of camouflaging : they can change the color of their skin in a few seconds to match their surroundings and completely disappear in the background. If you have never seen this, I invite you to spend 5 min enjoying this Ted Talk.


Introduction to Reinforcement Learning – Chapter 1

Reinforcement Learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. A learning agent can take actions that affect the state of the environment and have goals relating to the state of the environment. One of the challenges that arise in Reinforcement Learning, and not in other kinds of learning, is trade-off between exploration and exploitation. Of all the forms of Machine Learning, Reinforcement Learning is the closest to the kind of learning that humans and other animals do.


Introducing Pro-ML

Building machine learning solutions at scale remains an active area of experimentation for most organizations. While many companies are starting their initial machine learning pilots, few have a robust strategy to scale machine learning workflows. This issue is particularly challenging if we consider that, in the current market, machine learning research and development frameworks have evolved disproportionately faster than the corresponding infrastructure runtimes required to scale machine learning programs. With so little guidance available about how to build machine learning solutions at scale, an invaluable source becomes the experience of internet giants such as Uber, LinkedIn, Google, Netflix or Microsoft whose scalability requirements are exceedingly more complex than the ones faced by most companies. At LinkedIn, the roadblocks for delivering machine learning solutions at scale were becoming so critical that the company decided to create a separate initiative called Productive Machine Learning(Pro-ML) to address this challenge.


Importance of Distance Metrics in Machine Learning Modelling

A number of Machine Learning Algorithms – Supervised or Unsupervised, uses Distance Metrics to know the input data pattern in order to make any Data Based decision. A good distance metric helps in improving the performance of Classification, Clustering and Information Retrieval process significantly. In this article, we will discuss about different Distance Metrics and how do they help in Machine Learning Modelling.


I walk the (train) line – part deux – the weight loss continues

This is an example of breadth-first search of a graph. We will be using it to answer this question: Does a path exists between our source node and our destination node?


HyperNEAT: Powerful, Indirect Neural Network Evolution

Last week, I wrote an article about NEAT (NeuroEvolution of Augmenting Topologies) and we discussed a lot of the cool things that surrounded the algorithm. We also briefly touched upon how this older algorithm might even impact how we approach network building today, alluding to the fact that neural networks need not be built entirely by hand. Today, we are going to dive into a different approach to neuroevolution, an extension of NEAT called HyperNEAT. NEAT, as you might remember, had a direct encoding for its network structure. This was so that networks could be more intuitively evolved, node by node and connection by connection. HyperNEAT drops this idea because in order to evolve a network like the brain (with billions of neurons), one would need a much faster way of evolving that structure.


https://towardsdatascience.com/a-practical-guide-to-interpreting-and-visualising-support-vector-machines-97d2a5b0564e

This article contains the following sections:
1. Introduction to Linear Models, SVM’s and Kernels
2. Interpreting high dimensional engineered feature spaces which are utilising SVM kernels…
3. Techniques for evaluating the performance of high dimensional classification boundaries
4. Practical options for working with large class imbalances
5. How much data you need to train an SVM


How Blockchain Will Disrupt Data Science: 5 Blockchain Use Cases in Big Data

• Ensuring Trust (Data Integrity)
• Preventing Malicious Activities
• Making Predictions (Predictive Analysis)
• Real-Time Data Analysis
• Manage Data Sharing


Get Started with PyTorch – Learn How to Build Quick & Accurate Neural Networks

PyTorch v TensorFlow – how many times have you seen this polarizing question pop up on social media? The rise of deep learning in recent times has been fuelled by the popularity of these frameworks. There are staunch supporters of both, but a clear winner has started to emerge in the last year. PyTorch was one of the most popular frameworks in 2018. It quickly became the preferred go-to deep learning framework among researchers in both academia and the industry. After using PyTorch for the last few weeks, I can confirm that it is highly flexible and an easy-to-use deep learning library.


Generating Synthetic Data Sets with ‘synthpop’ in R

Synthpop – A great music genre and an aptly named R package for synthesising population data. I recently came across this package while looking for an easy way to synthesise unit record data sets for public release. The goal is to generate a data set which contains no real units, therefore safe for public release and retains the structure of the data. From which, any inference returns the same conclusion as the original. This will be a quick look into synthesising data, some challenges that can arise from common data structures and some things to watch out for.


Forecasting Exchange Rates Using ARIMA In Python

Nearly all sectors use time series data to forecast future time points. Forecasting future can assist analysts and management in making better calculated decisions to maximise returns and minimise risks. I will be demonstrating how we can forecast exchange rates in this article. If you are new to finance and want to understand what exchange rates are then please read my article ‘Best way To Learn Finance? Understand Market Data’. It provides a basic overview of market data. Exchange rates are dependent on a range of factors such as supply and demand, government policies, country’s growth rates etc. For more information on economical indicators that can impact exchange rates, please have a look at my article ‘Everything You Need To Know To Assess And Compare Countries’.


Explainable Artificial Intelligence

We outline the necessity of explainable AI, discuss some of the methods in academia, take a look at explainability vs accuracy, investigate use cases, and more.
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