A survey requesting feedback from data scientists on their opinion of what an interesting result is. The survey is anonymous, has only a single mandatory question, and takes only 5 minutes.
Art has always been deep for those who appreciate it... but now, more than ever, deep learning is making a real impact on the art world. Check out this graduate course, and its freely-available resources, focusing on this very topic.
Today is the 80th anniversary of the death of Karl Pearson, one of the founding father of statistics (correlation coefficient, principal components, the p-value, and much more). He was also deeply involved with eugenics, a jarring reminder that truth often comes bundled with a measure of darkness.
Dealing with huge datasets can be tricky, especially the data cleaning process. One of such processing is de-duplication, find out how you can solve this using the statistical techniques.
This post is an overview and discussion of Microsoft's increasing investment in, and approach to, artificial intelligence, and how their philosophy differs from their competitors.
As the rampant growth of data science continues across industries, the opportunities are plenty for both aspiring and expert data scientists. Here is an overview of data science industries, opportunities and work locations.
A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning. Python is the clear target here, but general principles are transferable.
Want to make a career change to Data Science using python? Well learning anything on your own can be a challenge & a little guidance could be a great help, that is exactly what this article will provide you with.
Your company needs a data scientist... doesn't it? It very well may not, but you need to know either way. Read on to determine whether or not your company could benefit from the skills of an on-board data scientist.
The first in a series of tutorial posts on using Deep Learning for chatbots, this covers some of the techniques being used to build conversational agents, and goes from the current state of affairs through to what is and is not possible.
Either you are a researcher, start-up or big organization who wants to use machine learning, you will need the right tools to make it happen. Here is a list of the most popular frameworks for machine learning.
The idea of using artificial intelligence for the crime prevention has been around for more than a decade. In this post, we present four examples, including how using analytics, we can prevent a criminal from re-offending.
It’s been well documented that women don’t come close to parity in STEM fields with their counterparts. Could the rise of big data and data science offer women a clearer path to success in technology? Here’s a list of 12 inspiring women who work in big data and data
Covers recommender systems comprehensively, both fundamentals and advanced topics, organized into: Algorithms and evaluation, recommendations in specific domains and contexts, and advanced topics and applications.
Self-service analytics is likely to spread in all the business layers, and with proper care to avoid certain risks, the culture of self-service analytics will help all organizations.
What will likely become known as the seminal book on deep learning is finally finished, with the online version finalized and freely-accessible to all those interested in mastering deep neural networks.
A new data science report with survey results related to the success and challenges of data scientists, and where data science is going as a discipline.
JSU is among the first minority serving institutions to create a Big Data focused doctoral and graduate program for MS and PhD in Computational and Data-Enabled Science and Engineering - apply now.
With the rise of neural network in data science, the demand for computationally extensive machines lead to GPUs. Learn how you can get started with GPUs & algorithms which could leverage them.
H2O is feature-rich open source machine learning platform known for its R and Spark integration and it’s ease of use. This is an overview of using H2O deep learning for data science with the Internet of Things.
With the number of people claiming to be a data scientist growing, the “true” data scientists are becoming hard to find. Here your guide identify the clues to catch a bad data scientists.
When Does Deep Learning Work Better Than SVMs or Random Forests®?
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