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Nearest Neighbor Methods


Machine Learning for a Better Developer Experience

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Imagine having to go through 2.5GB of log entries from a failed software build -- 3 million lines -- to search for a bug or a regression that happened on line 1M. However, one smart approach to make it tractable might be to diff the lines against a recent successful build, with the hope that the bug produces unusual lines in the logs. Standard md5 diff would run quickly but still produce at least hundreds of thousands candidate lines to look through because it surfaces character-level differences between lines. Fuzzy diffing using k-nearest neighbors clustering from machine learning (the kind of thing logreduce does) produces around 40,000 candidate lines but takes an hour to complete. Our solution produces 20,000 candidate lines in 20 min of computing -- and thanks to the magic of open source, it's only about a hundred lines of Python code.


K-NN classification. Why it is interesting for beginners?

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According to experience, this is one of interesting and easy to use an algorithm which makes classification very easy. What is the K-Nearest Neighbour (KNN) algorithm? What is the need of KNN algorithm? When we use KNN algorithm? How to select K for the KNN algorithm.


KNNImputer

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The idea in kNN methods is to identify'k' samples in the dataset that are similar or close in the space. Then we use these'k' samples to estimate the value of the missing data points. Each sample's missing values are imputed using the mean value of the'k'-neighbors found in the dataset. Let's look at an example to understand this. Consider a pair of observations in a two-dimensional space (2,0), (2,2), (3,3).


Every Machine Learning Algorithm Can Be Represented as a Neural Network

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It seems that all of the work in machine learning -- starting from early research in the 1950s -- cumulated with the creation of the neural network. Successively, algorithm after new algorithm were proposed, from logistic regression to support vector machines, but the neural network is, very literally, the algorithm of algorithms and the pinnacle of machine learning. It's a universal generalization of what machine learning is, instead of one attempt of doing it. In this sense, it is more of a framework and a concept than simply an algorithm, and this is evident given the massive amount of freedom in constructing neural networks -- hidden layer & node counts, activation functions, optimizers, loss functions, network types (convolutional, recurrent, etc.), and specialized layers (batch norm, dropout, etc.), to name a few. From this perspective of neural networks being a concept rather than a rigid algorithm comes a very interesting corollary: any machine learning algorithm, be it decision trees or k-nearest neighbors, can be represented using a neural network.


Recommender Systems from Learned Embeddings

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We will use Movie ID and User ID to generate their corresponding embeddings. These embeddings are generated through the model training process along with other parameters. Once we have the embeddings, we build a K-Nearest Neighbor (KNN) model. Then whenever there is a user, we can get that user's embedding from our Neural Network model. We use this embedding to lookup in the KNN database and recommend top -- K movies to this user.


Overcoming an Imbalanced Dataset using Oversampling.

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How oversampling yielded great results for classifying cases of Sexual Harassment. When it comes to data science, sexual harassment is an imbalanced data problem, meaning there are few (known) instances of harassment in the entire dataset. An imbalanced problem is defined as a dataset which has disproportional class counts. Oversampling is one way to combat this by creating synthetic minority samples. SMOTE -- Synthetic Minority Over-sampling Technique -- is a common oversampling method widely used in machine learning with imbalanced high-dimensional datasets using Oversampling.


9 Key Machine Learning Algorithms Explained in Plain English

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Machine learning [https://gum.co/pGjwd] is changing the world. Google uses machine learning to suggest search results to users. Netflix uses it to recommend movies for you to watch. Facebook uses machine learning to suggest people you may know. Machine learning has never been more important. At the same time, understanding machine learning is hard. The field is full of jargon. And the number of different ML algorithms grows each year. This article will introduce you to the fundamental concepts


K-Nearest Neighbors (KNN) Algorithm In Machine Learning

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K-Nearest Neighbors (KNN) Algoritm in Machine Learning will help you to master all the concepts of KNN. KNN algorithm is one of the simplest, non-parametric, lazy classification learning algorithm. Its purpose is to use a dataset in which the data points are separated into several classes to predict the classification of a new sample point. This tutorial on "KNN Algoritm in Machine Learning" will help you to master all the concepts of K-nearest neighbors. Its purpose is to use a dataset in which the data points are separated into several classes to predict the classification of a new sample point.


Understanding Machine learning

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Artificial intelligence is everywhere in the press. Machine learning is everywhere in companies. What is the link between these three disciplines, and above all, what differentiates them? Indeed, in the common imagination, when we talk about artificial intelligence, we mean by this a program that can perform human tasks, learning on its own. However, AI as defined in the industry is rather "more or less advanced algorithms which imitate human actions".


Dex-Net AR uses Apple's ARKit to train robots to grasp objects

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UC Berkeley AI researchers are using an iPhone X and Apple's ARKit to train a robotic arm how to grasp an object. ARKit creates point clouds from data generated by moving an RGB camera around an object for two minutes. Robotic grasping is a particular robotics subfield focused on the challenge of teaching a robot to pick up, move, manipulate, or grasp an object. The Dexterity Network, or Dex-Net, research project at UC Berkeley's Autolab dates back to 2017 and includes open source training data sets and pretrained models for robotic grasping in an ecommerce bin-picking scenario. The ability for robots to quickly learn how to grasp objects has a big impact on how automated warehouses like Amazon fulfillment centers can become.