Summary: There are several approaches to reducing the cost of training data for AI, one of which is to get it for free. Here are some excellent sources.

Recently we wrote that training data (not just data in general) is the new oil. It’s the difficulty and expense of acquiring labeled training data that causes many deep learning projects to be abandoned.

It also matters a great deal just how good you want your new deep learning app to be. A 2016 study by…

Guest blog post by Zied HY. Zied is Senior Data Scientist at Capgemini Consulting. He is specialized in building predictive models utilizing both traditional statistical methods (Generalized Linear Models, Mixed Effects Models, Ridge, Lasso, etc.) and modern machine learning techniques (XGBoost, Random Forests, Kernel Methods, neural networks, etc.). Zied run some workshops for university students (ESSEC, HEC, Ecole polytechnique) interested in Data…

Summary:  The role of Analytics Translator was recently identified by McKinsey as the most important new role in analytics, and a key factor in the failure of analytic programs when the role is absent.

The role of Analytics Translator was…

You won't learn this in textbooks, college classes, or data camps. Some of the material in this article is very advanced yet presented in simple English, with an Excel implementation for various statistical tests, and no arcane theory, jargon, or obscure theorems. It has a number of applications, in finance in particular. This article covers several topics under a unified approach, so it was not easy to find a title. In particular, we discuss:

• When the central limit theorem…

Full title: Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems. Published June 2, 2018. Author: Vincent Granville, PhD. (104 pages, 16 chapters.)

This book is intended for professionals in data science, computer science, operations research, statistics, machine learning, big data, and mathematics. In 100 pages, it covers many new topics, offering a fresh perspective on the subject. It is accessible to…

Join the largest community of machine learning (ML), deep learning, AI, data science, business analytics, BI, operations research, mathematical and statistical professionals: Sign up here. If instead, you are only interested in receiving our newsletter, you can subscribe here. There is no…

Let us consider the following equation:

Prove that

• x = log(Pi) = 1.14472988584... is a very good approximation of a solution, up to 10 digits.
• Using high…

Here is Rafael Knuth's story.

In 1992, I entered the job market and landed a job as an advertising copywriter for McDonald’s. I was tasked with ideating radio, TV and print advertisements to curb burger, fries and soft drink sales. The internet did not exist in the public domain back then, and my first laptop was actually a mechanical type writer. Around 2000, I became a freelance…

Here is our selection of recently featured articles and resources:

Featured Resources and Technical Contributions 

• Statistical Tests - When to use Which ? 
• The Artificial Neural Networks Handbook - Part…

For a person being from a non-statistical background the most confusing aspect of statistics, are always the fundamental statistical tests, and when to use which. This blog post is an attempt to mark out the difference between the most common tests, the use of null value hypothesis in these tests and outlining the conditions under which a particular test should be used.

Null Hypothesis and Testing

Before we venture on the difference between different tests, we…

I have been involved in teaching Data Science for a few years now (Oxford University - Data Science for Internet of Things and also online). Over the years, I have tried to improve my teaching .. and adopt ideas from other domains into my teaching

One such technique is Deliberate practice a technique which probably originated in the former Soviet Union to train world class athletes. Deliberate practise is also used in learning complex skills like playing the violin – which require…

Here is our selection of featured articles and resources posted recently:

Resources

• Solving Image Processing Problems with Python - Part 1
• Getting Started with NLP: Simple Topic Modeling in…

Summary: There are some interesting use cases where combining CNNs and RNN/LSTMs seems to make sense and a number of researchers pursuing this.  However, the latest trends in CNNs may make this obsolete.

There are things that just don’t seem to go together.  Take oil and water for instance.  Both valuable, but try putting them together? 

That was my reaction when I first came across the idea of combining CNNs (convolutional neural…

Here is our selection of featured articles posted in the last few days:

• Comparison of the Most Useful Text Processing APIs 
• A Comparative Analysis of Top 6 BI and Data Visualization Tools …

Andrew Ng is a great fan of reading research papers as a long term investment in your own study (On Life, Creativity, And Failure about Andrew Ng). Anyone who has worked in our field (AI, Machine Learning) can attest to that. AI is a complex and a rapidly evolving field. It’s a challenge to stay up to date with the latest technical details.

Based on my experience, in this post, I discuss how you can stay up to date by learning from the community. From a personal perspective, I work in…

Summary:  Our recent series of articles on AI strategies shows the options available for the strategic direction of your AI-first company.  Here are some thoughts on moving from strategy to implementation, including some useful tools to help in planning.

Hope you’ve…

What do experienced data scientists know that beginner data scientists don't know? Here is a quick overview.

1. Automating tasks. Writing code that writes code.
2. Outsourcing tasks to junior members or to consultants.
3. Managing people, hiring the right people, managing managers who report to you.
4. Training colleagues who might not be tech-savvy. Be an adviser for senior managers.
5. Identifying the right tools and assessing the benefits and minuses of…

Guest blog by Seth Dobrin and Daniel Hernandez.

Companies have been sold on the alchemy of data science. They have been promised transformative results. They modeled their expectations after their favorite digital-born companies. They have piled a ton of money into hiring expensive data scientists and ML engineers. They invested heavily in software…

Insights and Advice from Data Science Leaders and Key Influencers. Paperback – July 13, 2018. By Matt Corey.

The Data Scientist’s Book of Quotes includes over 300 insightful and inspiring quotes from the world’s leading Data Science thought leaders and key influencers across the world, including Andrew Ng, Bernard Marr, Vincent Granville, Carla Gentry, Cathy O’Neil and Hilary Mason. The Data Scientist role is one of the most pivotal and disruptive roles in today’s global…

In 2018 Fast Company declared the Data Scientist the best job for the third year in a row, which I wholeheartedly agree with (besides the Director of Fun at the York National Railway Museum), however the role of data scientist, as we know it, will soon have the same fate as the bowling pinsetters, chariot racers, and human alarm clocks.

In 2000-2010 data science was dominated by masters of herculean subjects, with PhDs in linear…