Tag

data science

A New Approach to Deep Learning

From Probabilistic Deep Learning with Python by Oliver Dürr, Beate Sick, and Elvis Murina


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The Computer Vision Pipeline, Part 5: Classifier learning algorithms and conclusion

From Deep Learning for Vision Systems by Mohamed Elgendy

Aggregating Your Data with Spark

From Spark in Action, Second Edition by Jean-Georges Perrin

Basic Time-Series Forecasting

From Machine Learning for Business by Doug Hudgeon and Richard Nichol

This article covers basic time-series forecasting: what it is and why it’s a tough problem.

Function Pipelines for Mapping Complex Transformations

From Mastering Large Datasets with Python by J.T. Wolohan

This article covers

· Using map to do complex data transformations

· Chaining together small functions into pipelines

· Applying these pipelines in parallel on large datasets

The Computer Vision Pipeline, Part 4: feature extraction

From Deep Learning for Vision Systems by Mohamed Elgendy

In this part, we will take a look at feature extraction—a core component of the computer vision pipeline.

Combining Human and Machine Intelligence

From Human-in-the-Loop Machine Learning by Robert Munro


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Maximise Customer Retention

From Fighting Churn with Data by Carl Gold


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Is my Problem a Graph Problem?

From Graph Databases in Action by Dave Bechberger

In this article, we’ll review what makes a problem a good graph use case. We’ll start by examining a few general categories of problems and discussing why they might make for good graph use case.  Finally, we’ll analyze a general framework that we can use to help us decide if our problem is a good graph use case.

The Data Scientist’s Survival Guide

From Build Your Career in Data Science by Emily Robinson and Jacqueline Nolis


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