5 Reasons Machine Learning Applications Need a Better Lambda Architecture
The Lambda Architecture enables a continuous processing of real-time data. It is a painful process that gets the job done, but at a great cost. Here is a simplified solution called as Lambda-R (ƛ-R) for the Relational Lambda.
on Jun 2, 2016 in Applications, Lambda Architecture, Machine Learning, Monte Zweben, Splice Machine
5 Ways in Which Big Data Can Help Leverage Customer Data
Every business enterprise realizes the importance of big data but rarely puts the customer data that they possess to good use. Here are few ways enterprises can leverage customer data.
on May 25, 2016 in Analytics, Big Data, Data Management, Data Mining
Let Me Hear Your Voice and I’ll Tell You How You Feel
This post provides an overview of a voice tone analyzer implemented as part of a cohesive emotion detection system, directly from the researcher and architect.
on May 24, 2016 in Artificial Intelligence, Deep Learning, Emotion
10 Must Have Data Science Skills, Updated
An updated look at the state of the data science landscape, and the skills - both technical and non-technical - that are absolutely required to make it as a data scientist.
on May 23, 2016 in Advice, Books, Data Science Skills, Data Scientist, MOOC
How to Explain Machine Learning to a Software Engineer
How do you explain what machine learning is to the uninitiated software engineer? Read on for one perspective on doing so.
on May 20, 2016 in Automating, Machine Learning, Software Engineer
Tips for Data Scientists: Think Like a Business Executive
Thinking like a Data Scientist is important; it puts businesses and business leaders in an analytical frame of mind. But it is also important for Data Scientists to be able to think like business executives. Read on to find out why.
on May 18, 2016 in Advice, Analytics, Data Scientist
Embrace the Random: A Case for Randomizing Acceptance of Borderline Papers
A case for using randomization in the selection of borderline academic papers, a particular use case which has parallels with many other possible scenarios.
on May 16, 2016 in Academics, ICML, NIPS, Random, Randomization
Practical skills that practical data scientists need
The long story short, data scientist needs to be capable of solving business analytics problems. Learn more about the skill-set you need to master to achieve so.
on May 13, 2016 in Business Context, Data Scientist, Mathematics, Skills, SQL
Are Deep Neural Networks Creative?
Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?
on May 12, 2016 in Artificial Intelligence, Deep Learning, Generative Adversarial Network, Generative Models, Recurrent Neural Networks, Reinforcement Learning, Zachary Lipton
Why Implement Machine Learning Algorithms From Scratch?
Even with machine learning libraries covering almost any algorithm implementation you could imagine, there are often still good reasons to write your own. Read on to find out what these reasons are.
on May 6, 2016 in Algorithms, Machine Learning
How Much do Analytics Salaries Increase when Changing Jobs?
A data-informed analysis of analytics career salaries and their increase when changing jobs.
on May 4, 2016 in Analytics, Burtch Works, Career, Salary
A Data Science Approach to Writing a Good GitHub README
Readme is the first file every user will look for, whenever they are checking out the code repository. Learn, what you should write inside your readme files and analyze your existing files effectiveness.
on May 4, 2016 in Algorithmia, GitHub, Text Mining
Datasets Over Algorithms
The average elapsed time between key algorithm proposals and corresponding advances is about 18 years; the average elapsed time between key dataset availabilities and corresponding advances is less than 3 years, 6 times faster.
on May 3, 2016 in Algorithms, Datasets
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