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
Top 10 Open Dataset Resources on Github
The top open dataset repositories on Github include a variety of data, freely available for use by researchers, practitioners, and students alike.
on May 31, 2016 in Datasets, GitHub, Machine Learning, Open Data
Predicting Popularity of Online Content
A look at predicting what makes online content popular, with a particular focus on images, especially selfies.
on May 30, 2016 in Prediction, Selfie
Free eBook: Healthcare Social Media Analytics and Marketing
Get your free copy of a new ebook outlining social media marketing and analytics strategies (including code) for healthcare professionals.
on May 27, 2016 in Free ebook
A Concise Overview of Standard Model-fitting Methods
A very concise overview of 4 standard model-fitting methods, focusing on their differences: closed-form equations, gradient descent, stochastic gradient descent, and mini-batch learning.
on May 27, 2016 in Cost Function, Gradient Descent, Machine Learning, Sebastian Raschka
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
5 Machine Learning Projects You Can No Longer Overlook
We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.
on May 19, 2016 in Data Cleaning, Deep Learning, Machine Learning, Open Source, Overlook, Pandas, Python, scikit-learn, Theano
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
The Amazing Power of Word Vectors
A fantastic overview of several now-classic papers on word2vec, the work of Mikolov et al. at Google on efficient vector representations of words, and what you can do with them.
on May 18, 2016 in Distributed Representation, NLP, word2vec
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
Troubleshooting Neural Networks: What is Wrong When My Error Increases?
An overview of some of the things that could lead to an increased error rate in neural network implementations.
on May 13, 2016 in Deep Learning, Neural Networks, Overfitting
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
Deep Learning and Neuromorphic Chips
The 3 main ingredients to creating artificial intelligence are hardware, software, and data, and while we have focused historically on improving software and data, what if, instead, the hardware was drastically changed?
on May 12, 2016 in AI, Brain, Deep Learning, Neural Networks
Implementing Neural Networks in Javascript
Javascript is one of the most prevalent and fastest growing languages in existence today. Get a quick introduction to implementing neural networks in the language, and direction on where to go from here.
on May 12, 2016 in Javascript, MNIST, Neural Networks
Meet the 11 Big Data & Data Science Leaders on LinkedIn
In this post, we present a list of popular data science leaders on LinkedIn. Follow these leaders who will keep you in touch with the latest Data Science happenings!
on May 6, 2016 in About Gregory Piatetsky, Bernard Marr, Big Data, Data Scientist, DJ Patil, Hilary Mason, Influencers, LinkedIn, Tom Davenport
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
How to Network and Build a Personal Brand in Data Science
SpringBoard shares some ideas on how to network and build a data career, as taken from a new guide they have put together on the topic.
on May 2, 2016 in Career, KDD, Mentorship, Strata
How to Use Cohort Analysis to Improve Customer Retention
Cohort analysis is a subset of behavioral analytics that takes the user data and breaks them into related groups for analysis. Let’s understand using cohort analysis with an example of daily cohort of app users.
on May 2, 2016 in Churn, CleverTap, Customer Analytics, Customer Behavior
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