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Exploring All Types of Machine Learning Algorithms

Pickl AI

Summary: Machine Learning algorithms enable systems to learn from data and improve over time. These algorithms are integral to applications like recommendations and spam detection, shaping our interactions with technology daily. These intelligent predictions are powered by various Machine Learning algorithms.

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Clustering with Scikit-Learn: a Gentle Introduction

Towards AI

Learn how to apply state-of-the-art clustering algorithms efficiently and boost your machine-learning skills.Image source: unsplash.com. This is called clustering. In Data Science, clustering is used to group similar instances together, discovering patterns, hidden structures, and fundamental relationships within a dataset.

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KNNs & K-Means: The Superior Alternative to Clustering & Classification.

Towards AI

Let’s discuss two popular ML algorithms, KNNs and K-Means. We will discuss KNNs, also known as K-Nearest Neighbours and K-Means Clustering. They are both ML Algorithms, and we’ll explore them more in detail in a bit. They are both ML Algorithms, and we’ll explore them more in detail in a bit.

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K-Means From Scratch: How The Cluster Magic Works

Towards AI

Reverse Engineering The SciKit ImplementationPhoto by Mel Poole on Unsplash Understanding how an algorithm works is interesting as it provides some insights into why an implementation may not be as one would expect. This is not always easy to do as some algorithms have stochastic components. Let’s dive deeper into the algorithm.

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Google Research, 2022 & beyond: Algorithmic advances

Google Research AI blog

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

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Scrambling Eggs for Spotify with Knuth's Fibonacci Hashing

Hacker News

In this blog post, we explore Spotify's journey from using the Fisher-Yates shuffle to a more sophisticated song shuffling algorithm that prevents clustering of tracks by the same artist. We then connect this challenge to Fibonacci hashing, and propose a novel, evenly distributed artist shuffling method.

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Differentially private clustering for large-scale datasets

Google Research AI blog

Posted by Vincent Cohen-Addad and Alessandro Epasto, Research Scientists, Google Research, Graph Mining team Clustering is a central problem in unsupervised machine learning (ML) with many applications across domains in both industry and academic research more broadly. When clustering is applied to personal data (e.g.,