Dark Knowledge Distilled from Neural Network
Geoff Hinton never stopped generating new ideas. This post is a review of his research on “dark knowledge”. What’s that supposed to mean?
on May 26, 2015 in Dark Knowledge, Deep Learning, Geoff Hinton, Neural Networks, Ran Bi
Top 10 Data Mining Algorithms, Explained
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.
on May 21, 2015 in Algorithms, Apriori, Bayesian, Boosting, C4.5, CART, Data Mining, Explained, K-means, K-nearest neighbors, Naive Bayes, Page Rank, Support Vector Machines, Top 10
How to reduce Data Hoarding, get Better Visualizations and Decisions
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.
on May 21, 2015 in Alex Jones, Dashboard, Data Visualization, Linear Discriminant Analysis, PCA
How to Lead a Data Science Contest without Reading the Data
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.
on May 17, 2015 in Accuracy, Benchmark, Competition, Kaggle, Model Performance
Seven Techniques for Data Dimensionality Reduction
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.
By Rosaria Silipo on May 14, 2015 in Data Processing, High-dimensional, Knime, Rosaria Silipo
Machine Learning Wars: Amazon vs Google vs BigML vs PredicSis
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.
on May 12, 2015 in Amazon, BigML, Google, Louis Dorard, Machine Learning, PredicSis
3 Things About Data Science You Won’t Find In Books
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.
on May 11, 2015 in Cross-validation, Data Preparation, Data Science, Feature Engineering, Feature Extraction, Overfitting
Guiding Principles to Build a Demand Forecast
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.
on May 4, 2015 in Forecasting, Lana Klein
How To Become a Data Scientist And Get Hired
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.
on May 1, 2015 in Business, Data Scientist, Hiring, Salary