&amp;quot;The future is here. It's just not evenly distributed yet.&amp;quot; - William Gibson :::: Follow this topic for fresh resources and ideas related to Data Science, Machine Learning, Algorithms and #bigdata :::: <a href="http://www.dataisbig.co" rel="nofollow">http://www.dataisbig.co</a>/
Always wanted to compete in a Kaggle competition, but not sure you have the right skill set? At DataCamp we created a free interactive tutorial to help you out!
Together with the team at Kaggle, we have developed this tutorial on how to apply Machine Learning techniques. Step by step, through fun coding challenges, the tutorial will teach you how to predict survival rate for Kaggle's Titanic competition using R and Machine Learning. The skills you'll learn in the tutorial can be applied across your Kaggle competitions
There are lots of awesome reading lists or posts that summarized materials related to Deep Learning. So why would I commit another one? Well, the primary objective is to develop a complete reading list that allows readers to build a solid academic and practical background of Deep Learning. And this list is developed while I'm preparing my Deep Learning workshop. My research is related to Deep Neural Networks (DNNs) in general. Hence, this posts tends to summary contributions in DNNs instead of generative models.
Police departments are increasingly turning to predictive analytics to help them fight crime, and the early returns are positive, with double-digit drops in crime rates reported in many cities. So what’s next? According to big data analytics experts, police departments could spend their time and money more effectively by giving deep learning algorithms a role in the dispatch room.
"Selecting the most important predictor variables that explains the major part of variance of the response variable can be key to identify and build high performing models. These techniques are powerful tools that can help reveal the large sediments of gold in your data.
Customer analytics (48%), operational analytics (21%), and fraud & compliance (21%) are the top three use cases for Big Data. Datameer’s analysis of the market also found that the global Hadoop market will grow from $1.5B in 2012 to $50.2B in 2020, and financial services, technology and telecommunications are the leading industries using big data solutions today
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