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The hardest parts of data science
The hardest part of data science is not building an accurate model or obtaining good, clean data, but defining feasible problems and coming up with reasonable ways of measuring solutions.
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Top KDnuggets tweets, Nov 16-22: Dilbert discovers the perfect chart; TensorFlow Disappoints – Google Deep Learning falls shallow
A standard #graph for any occasion! #Dilbert discovers the perfect chart; TensorFlow Disappoints - Google #DeepLearning falls shallow; All the #BigData tools and how to use them; KDnuggets #DataScience #Cartoon Caption Contest.
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Data-Planet Statistical Datasets
Data-Planet Statistical Datasets provides easy access to an extensive repository of standardized and structured statistical data, with more than 25 billion data points from more than 70 source organizations.
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5 Warning Signs that Turn Off Data Science Hiring Managers
Here are some warning signs that will prevent managers from hiring you for a Data Science position. If your resume has one or more of them, make an effort to remove the risk factors.
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Online course: Credit Risk Modeling
The course covers basic and advanced modeling, including stress testing Probability of Default (PD), Loss Given Default (LGD ) and Exposure At Default (EAD) models.
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Five Steps to Implement an Enterprise Data Lake
This guide helps you to initiate a new IT culture mapped to your business goals, and shows how do create an efficient data reservoir, what makes data more useful, and what are the cutting-edge tools/devices/applications you need.
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Deep Learning Adversarial Examples – Clarifying Misconceptions
Google scientist clarifies misconceptions and myths around Deep Learning Adversarial Examples, including: they do not occur in practice, Deep Learning is more vulnerable to them, they can be easily solved, and human brains make similar mistakes.
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Top stories for Jun 28 – Jul 4: Top 20 R packages by popularity; Nine Laws of Data Mining
Top 20 R packages by popularity; Top 20 R Machine Learning and Data Science packages; Nine Laws of Data Mining; The missing D in Data Science.
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Using Ensembles in Kaggle Data Science Competitions- Part 3
Earlier, we showed how to create stacked ensembles with stacked generalization and out-of-fold predictions. Now we'll learn how to implement various stacking techniques.
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Excellent Tutorial on Sequence Learning using Recurrent Neural Networks
Excellent tutorial explaining Recurrent Neural Networks (RNNs) which hold great promise for learning general sequences, and have applications for text analysis, handwriting recognition and even machine translation.
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