Remove 2019 Remove Algorithm Remove Datasets
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Choosing the Right Clustering Algorithm for your Dataset

KDnuggets

Applying a clustering algorithm is much easier than selecting the best one. Each type offers pros and cons that must be considered if you’re striving for a tidy cluster structure.

Algorithm 116
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Apache Spark vs MapReduce: A Detailed Comparison

Knowledge Hut

According to the marketanalysis.com report forecast, the global Apache Spark market will grow at a CAGR of 67% between 2019 and 2022. billion (2019 – 2022). You can view the same data as both graphs and collections, transform and join graphs with RDDs efficiently, and write custom iterative graph algorithms using the Pregel API.

Hadoop 96
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100+ Machine Learning Datasets Curated For You

ProjectPro

And honestly, there are a lot of real-world machine learning datasets around you that you can opt to start practicing your fundamental data science and machine learning skills, even without having to complete a comprehensive data science or machine learning course. Table of Contents What is a dataset in machine learning?

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Detecting Speech and Music in Audio Content

Netflix Tech

Practical use cases for speech & music activity Audio dataset preparation Speech & music activity is an important preprocessing step to prepare corpora for training. Nevertheless, noisy labels allow us to increase the scale of the dataset with minimal manual efforts and potentially generalize better across different types of content.

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8 Best Python Data Science Books [Beginners and Professionals]

Knowledge Hut

This book's publisher is "No Starch Press," and the second edition was released on November 12, 2019. Let’s study them further below: Machine learning : Tools for machine learning are algorithmic uses of artificial intelligence that enable systems to learn and advance without a lot of human input.

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Scikit-Learn & More for Synthetic Dataset Generation for Machine Learning

KDnuggets

While mature algorithms and extensive open-source libraries are widely available for machine learning practitioners, sufficient data to apply these techniques remains a core challenge. Discover how to leverage scikit-learn and other tools to generate synthetic data appropriate for optimizing and fine-tuning your models.

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Data Engineering: Fast Spatial Joins Across ~2 Billion Rows on a Single Old GPU

Towards Data Science

Comparing the performance of ORC and Parquet on spatial joins across 2 Billion rows on an old Nvidia GeForce GTX 1060 GPU on a local machine Photo by Clay Banks on Unsplash Over the past few weeks I have been digging a bit deeper into the advances that GPU data processing libraries have made since I last focused on it in 2019.