2015 May Tutorials, Overviews, How-Tos
All (110) | Courses, Education (5) | Meetings (8) | News, Features (22) | Opinions, Interviews, Reports (37) | Publications (8) | Software (6) | Top Tweets (4) | Tutorials, Overviews, How-Tos (9) | Webcasts (11)
- Dark Knowledge Distilled from Neural Network - May 26, 2015.
Geoff Hinton never stopped generating new ideas. This post is a review of his research on “dark knowledge”. What’s that supposed to mean?
- Top 10 Data Mining Algorithms, Explained - May 21, 2015.
Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.
- How to reduce Data Hoarding, get Better Visualizations and Decisions - May 21, 2015.
Creating a hodge-podge of pretty pictures of every datapoint is a guaranteed way to destroy the value of a visualization. We examine how to reduce such data hoarding and improve decisions.
- How to Lead a Data Science Contest without Reading the Data - May 17, 2015.
We examine a “wacky” boosting method that lets you climb the public leaderboard without even looking at the data . But there is a catch, so read on before trying to win Kaggle competitions with this approach.
- Seven Techniques for Data Dimensionality Reduction - May 14, 2015.
Performing data mining with high dimensional data sets. Comparative study of different feature selection techniques like Missing Values Ratio, Low Variance Filter, PCA, Random Forests / Ensemble Trees etc.
- Machine Learning Wars: Amazon vs Google vs BigML vs PredicSis - May 12, 2015.
Comparing 4 Machine Learning APIs: Amazon Machine Learning, BigML, Google Prediction API and PredicSis on a real data from Kaggle, we find the most accurate, the fastest, the best tradeoff, and a surprise last place.
- 3 Things About Data Science You Won’t Find In Books - May 11, 2015.
There are many courses on Data Science that teach the latest logistic regression or deep learning methods, but what happens in practice? Data Scientist shares his main practical insights that are not taught in universities.
- Guiding Principles to Build a Demand Forecast - May 4, 2015.
Demand forecasting is key for many industries, including finance, healthcare, and retails, and it is one of the most challenging tasks for predictive analytics. We review challenges and guiding principles of demand forecasting.
- How To Become a Data Scientist And Get Hired - May 1, 2015.
A data scientist should be able to choose the right technology, understand the business context and solve a wide range of problems. To hire the the right data scientist, check the tips list in the post.