Opinions, Interviews, Reports
We welcome guest blogs and opinions - see submission guidelines.
Latest:
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Holy Grail of AI for Enterprise — Explainable AI - Oct 19, 2018.
Explainable AI (XAI) is an emerging branch of AI where AI systems are made to explain the reasoning behind every decision made by them. We investigate some of its key benefits and design principles. -
New Jobs Sure to Emerge Alongside Artificial Intelligence - Oct 18, 2018.
There’s a lot of doomsaying about AI pushing humans out of jobs and destroying entire industries. Is it as bad as all that? Maybe not! -
Graphs Are The Next Frontier In Data Science - Oct 18, 2018.
GraphConnect 2018, Neo4j’s bi-annual conference, was held in New York City in mid-September. Read about what happened, and why graphs are the next big thing in data science. -
Music for Data Scientists? Music by Data Scientists? …What…?! - Oct 17, 2018.
Introducing Mean Reversion, an NYC-based songwriting duo comprising of data scientist Foster Provost and statistician Cliff Hurvich. -
Choose Your Own Adventure – Analytics On-boarding - Oct 15, 2018.
Lower the barriers to productivity by employing a “Choose your own adventure” approach to on-boarding your new analytics team members. -
Machine Reading Comprehension: Learning to Ask & Answer - Oct 11, 2018.
Investigating the dual ask-answer network, covering the embedding, encoding, attention and output layer, as well as the loss function, with code examples to help you get started. -
Evaluating the Business Value of Predictive Models in Python and R - Oct 11, 2018.
In these blogs for R and python we explain four valuable evaluation plots to assess the business value of a predictive model. We show how you can easily create these plots and help you to explain your predictive model to non-techies. -
Top 8 Python Machine Learning Libraries - Oct 9, 2018.
Part 1 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science. -
How To Learn Data Science If You’re Broke - Oct 9, 2018.
A first-hand account on how to learn data science on a budget, with advice covering useful resources, a recommended curriculum, typical concepts, building a portfolio and more. -
Semantic Interoperability: Are you training your AI by mixing data sources that look the same but aren’t? - Oct 9, 2018.
Semantic interoperability is a challenge in AI systems, especially since data has become increasingly more complex. The other issue is that semantic interoperability may be compromised when people use the same system differently. -
BIG, small or Right Data: Which is the proper focus? - Oct 8, 2018.
For most businesses, having and using big data is either impossible, impractical, costly to justify, or difficult to outsource due to the over demand of qualified resources. So, what are the benefits of using small data? -
Things you should know when traveling via the Big Data Engineering hype-train - Oct 8, 2018.
Maybe you want to join the Big Data world? Or maybe you are already there and want to validate your knowledge? Or maybe you just want to know what Big Data Engineers do and what skills they use? If so, you may find the following article quite useful. -
Basic Image Data Analysis Using Python – Part 4 - Oct 5, 2018.
Accessing the internal component of digital images using Python packages helps the user understand its properties, as well as its nature. -
Why do I Call Myself a Data Scientist? - Oct 5, 2018.
Claimed as the “sexiest job of the 21st Century” here I’ll discuss the reasons for my proclamation as a Data Scientist, beyond the hype. -
3 Stages of Creating Smart - Oct 4, 2018.
The technology is advancing at a pace that should enable any company to create “smart” products, things and spaces. But how does one go about actually creating smart? -
Top 3 Trends in Deep Learning - Oct 3, 2018.
We investigate the intermediate stage of deep learning, and the trends that are emerging in response to the challenges at this stage, including Interoperability and the multi-deployment options. -
How to Create a Simple Neural Network in Python - Oct 2, 2018.
The best way to understand how neural networks work is to create one yourself. This article will demonstrate how to do just that. -
5 Reasons Why You Should Use Cross-Validation in Your Data Science Projects - Oct 2, 2018.
In cross-validation, we do more than one split. We can do 3, 5, 10 or any K number of splits. Those splits called Folds, and there are many strategies we can create these folds with. -
A Right to Reasonable Inferences - Oct 1, 2018.
As shown in this paper, individuals are granted little control over how their personal data is used to draw inferences about them. Compared to other types, inferences are effectively ‘economy class’ personal data in the General Data Protection Regulation (GDPR).
September:
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Learning mathematics of Machine Learning: bridging the gap
We outline the four key areas of Maths in Machine Learning and begin to answer the question: how can we start with high school maths and use that knowledge to bridge the gap with maths for AI and Machine Learning? - ODSC India Highlights: Deep Learning Revolution in Speech, AI Engineer vs Data Scientist, and Reinforcement Learning for Enterprise
- What If the Data Tells You to Be Racist? When Algorithms Explicitly Penalize
- Datmo: the Open Source tool for tracking and reproducible Machine Learning experiments
- When Bayes, Ockham, and Shannon come together to define machine learning
- Building a Machine Learning Model through Trial and Error
- Diversity in Data Science: Overview and Strategy
- Cartoon: Where AI achieves excellence
- Data Capture – the Deep Learning Way
- A Deep (But Jargon Free) Dive Into Deep Learning
- Customer Data Unicorns: Why how we manage their data is the secret to finding, taming and riding them
- Deep Learning on the Edge
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How many data scientists are there and is there a shortage?
We examine the famous McKinsey prediction from 2011 and look into whether there a shortage of people with analytical expertise and estimate how many Data Scientists are there.
- Free resources to learn Natural Language Processing
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A Winning Game Plan For Building Your Data Science Team
We need to understand the responsibilities, capabilities, expectations and competencies of the Data Engineer, Data Scientist and Business Stakeholder. - 10 Big Data Trends You Should Know
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You Aren’t So Smart: Cognitive Biases are Making Sure of It
Cognitive biases are tendencies to think in certain ways that can lead to systematic deviations from a standard of rationality or good judgment. They have all sorts of practical impacts on our lives, whether we want to admit it or not.
- Ethics + Data Science: opinion by DJ Patil, former US Chief Data Scientist
- The Growing Participation of Women in the Data Science Community
- The Data Science of “Someone Like You” or Sentiment Analysis of Adele’s Songs
- The Economics and Benefits of Artificial Intelligence
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Hadoop for Beginners
An introduction to Hadoop, a framework that enables you to store and process large data sets in parallel and distributed fashion.
- Key Takeaways from KDD 2018: a Deconfounder, Machine Learning at Pinterest, Knowledge Graph
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Journey to Machine Learning – 100 Days of ML Code
A personal account from Machine Learning enthusiast Avik Jain on his experiences of #100DaysOfMLCode, a challenge that encourages beginners to code and study machine learning for at least an hour, every day for 100 days.
- Don’t Use Dropout in Convolutional Networks
- What on earth is data science?
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5 Resources to Inspire Your Next Data Science Project
In this post, my intention is provide some useful tips and resources to springboard you into your next data science project.
- Cartoon: Labor Day in the year 2050