Subject

Data Science

Applied Natural Language Processing

From Real-World Natural Language Processing by Masato Hagiwara


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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

This article teaches you how to perform an aggregation using Apache Spark. You first look at the definition of an aggregation. You may already know and use aggregations in your job, and this might be a reminder for you. If this is the case, you can safely skim through it: Apache Spark’s aggregations are standard. The second part of this section shows you how to transform a SQL aggregation statement to Spark.

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

Neural Network Architectures

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

This article dives into neural network architectures and how get started implementing and using them.

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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