The Power of Data
Reflections on how data (or lack thereof) helps (or fails) policy makers in developing countries Foreword When I stood up to speak last Friday at the Steering Committee meeting between … Read more
Featured
Neural Networks I: Notation and building blocks
Gentle introduction on Neural Networks Nov 25, 2018 This series of posts on Neural Networks are part of the collection of notes during the Facebook PyTorch Challenge, previous to the Deep … Read more
Exploratory Data Analysis (EDA) techniques for kaggle competition beginners
A hands on guide for beginners on EDA and Data Science competitions Exploratory Data Analysis (EDA) is an approach to analysing data sets to summarize their main characteristics, often with … Read more
Blockchain can be the new paradigm of the net
The popularization of blockchain will not depend on the users understanding its operation but on the existence of friendly and effective applications that solve real problems. Nov 22, 2018 Historically, … Read more
Blogging with Hugo and Jupyter
I really love blogging with Hugo+Blogdown, but unfortunately Blogdown is still mostly restricted to R (although Python is now also possible using the reticulate package). Jupyter offers a great literate … Read more
Combating media bias with AWS Comprehend
Nov 19, 2018 Photo by Randy Colas on Unsplash In the world of fake news and ideology-driven subjective media coverage, it is questionable which sources of journalism can be considered “reliable”. … Read more
Becoming An Analytics Manager Isn’t A Promotion.
(Photo by rawpixel on Unsplash) Nov 18, 2018 It’s A Career Change. Starting out as a data scientist may be the modern version of becoming a rock star but no-one really … Read more
Training your staff in data science? Here’s how to pick the right programming language
Businesses from every sector are investing in a data science education programmes. Working at tech education company Decoded, I have found it fascinating to see the immense value data skills … Read more
Kaggle: TGS Salt Identification Challenge
Nov 13, 2018 A few weeks ago finished TGS Salt Identification Challenge on the Kaggle, a popular platform for data science competitions. The task was to accurately identify if a … Read more
DOGNET: can an AI model fool a human?
The experiment was simple: could a machine learning (ML) model produce Golden Retriever images that people would mistake for being real? The reason for choosing dogs… was because dogs are … Read more
Telling Apart AI and Humans: #2 Photo VS GAN generated image
If you missed the 1st installement of this series, Humans vs Robots is here. Prompted by advances in Generative Adversarial Networks (GAN), a year ago I tweeted a thread about … Read more
Data Apocalypse!
The future of data storage What is Data? How is it stored, processed, transferred? What is the cloud? Will we eventually run out of space?! These are the questions that populated … Read more
Quantum advantage
Quantum computing is becoming visible in the tech world. There are over a dozen of hardware companies, each trying to build their own quantum computer, from small startups like Xanadu … Read more
Installing Hadoop 3.1.0 multi-node cluster on Ubuntu 16.04 Step by Step
Image Source: www.mapr.com/products/apache-hadoop/ There are many links on the web about install Hadoop 3. Many of them are not working well or need improvements. This article is taken from the … Read more
PyTorch 101 for Dummies like Me
Nov 5, 2018 What is PyTorch? It’s a Python-based package to serve as a replacement for Numpy arrays and to provide a flexible library forDeep Learning Development Platform. As for the … Read more
The Austrian Quant: My Machine Learning Trading Algorithm Outperformed the SP500 For 10 Years
Austrian Quant The Austrian Quant is named after the Austrian School of Economics which serves as the inspiration for how I structured the portfolio. I designed a trading strategy composed … Read more
Getting started – Azure SQL Server Managed Instance
There are a lot of options for data scientists to store data in the Azure cloud. In this blog post I will cover the pros and cons of Azure SQL … Read more
Industrial strength Natural Language Processing
I have spent much of my career as a graduate student researcher, and now as a Data Scientist in the industry. One thing I have come to realize is that … Read more
Building a Sentiment Detection Bot with Google Cloud, a Chat Client, and Ruby.
Introduction In this series, I’ll explain how to create a chat bot that is capable of detecting sentiment, analyzing images, and finally having the basis of a evolving personality. This … Read more
Debugging a Machine Learning model written in TensorFlow and Keras
Things that could go wrong, and how to diagnose if they did. Oct 24, 2018 In this article, you get to look over my shoulder as I go about debugging a … Read more
Introduction to Linear Regression in Python
Basic concepts and mathematics There are two kinds of variables in a linear regression model: The input or predictor variable is the variable(s) that help predict the value of the … Read more
A line-by-line layman’s guide to Linear Regression using TensorFlow
Computing the Graph With generate_dataset() and linear_regression(), we are now ready to run the program and begin finding our optimal gradient W and bias b! [line 2, 3] x_batch, y_batch = … Read more
Perplexity Intuition (and Derivation)
The perplexity of a discrete probability distribution is defined as: from https://en.wikipedia.org/wiki/Perplexity where H(p) is the entropy of the distribution p(x) and x is a random variable over all possible … Read more
Telling Apart AI and Humans: #1 Humans VS Androids
ALife 2018 conference, © Lana Sinapayen Prompted by a video where people thought a human was actually a hyper-realistic robot, I decided to write about how to spot humanoid robots. … Read more
Neural Nets: From Linear Regression to Deep Nets
Neural networks, especially deep neural networks, have received a lot of attention over the last couple of years. They perform remarkably well on image and speech recognition and form the … Read more
SQL Server
Columnstore A columnstore index can provide a very high level of data compression, typically by 10 times, to significantly reduce your data warehouse storage cost. For analytics, a columnstore index … Read more
The future of data visualization
Tools to shape the future In many product announcements from Google, Apple and BMW, more and more data will be overlaid in our physical environments through augmented reality or projection. That … Read more
Waiting for Weekends: Some Insights on How to Select the Best Wine
There is a huge selection of wines on the market and as for a wine lover it is always a quest to select the best wine. US, France, Spain, Germany … Read more
The Best Public Datasets for Machine Learning
First, a couple of pointers to keep in mind when searching for datasets. According to Carnegie Mellon University: 1.- A high-quality dataset should not be messy, because you do not … Read more
The intuition behind Shannon’s Entropy
Now, back to our formula 3.49: The definition of Entropy for a probability distribution (from The Deep Learning Book) I(x) is the information content of X. I(x) itself is a random … Read more
Forecasting Exchange Rates Using ARIMA In Python
Sep 29, 2018 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 … Read more
‘I want to learn Artificial Intelligence and Machine Learning. Where can I start?’
I bought a plane ticket to the US with no return flight. I’d been studying for a year and I figured it was about time I started putting my skills … Read more
Object Detection using Deep Learning Approaches: An End to End Theoretical Perspective
Fast RCNN So the next idea from the same authors: Why not create convolution map of input image and then just select the regions from that convolutional map? Do we … Read more
Image Processing Class (EGBE443) #2 -Histogram
Computing the histogram In this section, the histogram was calculated by implementation of python programming code (Python 3.6). For python 3.6, There are a lot of common modules using in … Read more
Get system metrics for 5 min with Docker, Telegraf, Influxdb and Grafana
Hi all, there is a very quick guide how to configure a system monitoring for one or more servers using a modern stack of technologies like Grafana, Docker and Telegraf … Read more
Lazy Neural Networks
Before I get into solutions I think it is important to discuss some overarching themes of deep learning. Training Objectives Remember that when we create a neural network, what we … Read more
How to get fbprophet working on AWS Lambda
Solving package size issues of fbprophet serverless deployment Adi Goldstein / Unsplash I assume you’re reading this post because you’re looking for ways to use the awesome fbprophet (Facebook open … Read more
Machine Learning – Particle Swarm Optimization (PSO) and Twitter
We all live in a world where analyzing a massive set of unstructured data is becoming a business need. And the time we spend on the internet is basically the … Read more
Multi-Layer perceptron using Tensorflow
Sep 11, 2018 In this blog, we are going to build a neural network(multilayer perceptron) using TensorFlow and successfully train it to recognize digits in the image. Tensorflow is a … Read more
Diving into K-Means…
Sep 9, 2018 We have completed our first basic supervised learning model i.e. Linear Regression model in the last post here. Thus in this post we get started with the … Read more
3 approaches for backtesting historical data
Reading and processing data for statistical and quantitative analysis in trading Sep 8, 2018 Anyone interested in the statistical analysis of financial markets has the need to process historical data. Historical … Read more
Microsoft Big Data Overview
https://academy.microsoft.com/en-us/professional-program/tracks/big-data/ Block 1 – Data Fundamentals Learn data science basics. Explore topics like data queries, data analysis, data visualization and how statistics informs data science practices. Please choose from Course … Read more
Box Cox Transformation
When we do time series analysis, we are usually interested either in uncovering causal relationships (Does \(X_t\) influence \(Y_{t+1}\)?) or in getting the most accurate forecast possible. Especially in the … Read more
Why feature weights in a machine learning model are meaningless
Don’t make decisions based on the weights of an ML model Aug 31, 2018 As I see our customers fall in love with BigQuery ML, an old problem rises its head — I … Read more
Doing XGBoost hyper-parameter tuning the smart way — Part 1 of 2
Aug 29, 2018 Picture taken from Pixabay In this post and the next, we will look at one of the trickiest and most critical problems in Machine Learning (ML): Hyper-parameter tuning. … Read more
Introduction to stochastic control theory
I had my first contact with stochastic control theory in one of my Master’s courses about Continuous Time Finance. I found the subject really interesting and decided to write my … Read more
Automatic Image Quality Assessment in Python
Aug 28, 2018 Image quality is a notion that highly depends on observers. Generally, it is linked to the conditions in which it is viewed; therefore, it is a highly subjective … Read more
Neural Processes: Probabilistic Gaussian Process+Deep Learning
Neural Processes (NPs) caught my attention as they essentially are a neural network (NN) based probabilistic model which can represent a distribution over stochastic processes. So NPs combine elements from two worlds:
Deep Learning – neural networks are flexible non-linear functions which are straightforward to train
Gaussian Processes – GPs offer a probabilistic framework for learning a distribution over a wide class of non-linear functions
Opensourcing TransmogrifAI: Automated ML for Structured Data
Despite huge progress in machine learning over the past decade, building production-ready machine learning systems is still hard. Three years ago when we set out to build machine learning capabilities into the Salesforce platform, we learned that building enterprise-scale machine learning systems is even harder.
Program Sythesis: Can We Teach Computers to Write Code?
Can we teach computers to write code? This is the question that brings out an entire branch of research specialized in program synthesis. Programming is a demanding task that requires extensive knowledge, experience and not a frivolous degree of creativity.