Devin Schumacher

著者： devin schumacher
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  • サマリー

  • On this podcast you’ll find a mix of the #AskDevin show, educational content, rants on marketing and business, miscellaneous lunacy, interviews and fireside chats.
    Copyright 2022 All rights reserved.
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あらすじ・解説

On this podcast you’ll find a mix of the #AskDevin show, educational content, rants on marketing and business, miscellaneous lunacy, interviews and fireside chats.
Copyright 2022 All rights reserved.
リーダーシップ 経済学
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エピソード
  • Affinity Propagation Machine Learning Algorithm

    Affinity Propagation Machine Learning Algorithm
    2023/08/25
    Affinity Propagation, also known as AP, is a machine learning algorithm that helps group similar data points together. It does this by using a "vote" system, where each data point "votes" for other data points it believes are most similar to itself. It's like a big game of telephone, where each person whispers a message to the next person until everyone has heard it. In AP, data points pass messages to each other until they all agree on which data points are best to represent the different clusters. This algorithm is unsupervised, meaning it doesn't need any pre-labeled data. It figures out the optimal number of clusters and which data points belong to each cluster on its own. This can be incredibly helpful to find patterns in your data and make predictions about new data point values. Using Affinity Propagation can make your job easier by quickly and accurately grouping similar data points together without needing any prior knowledge about the data. So next time you're trying to organize a big group of people, think of Affinity Propagation and its "vote" system to help you group people together based on their similarities!
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    5 分
  • Adam Machine Learning Algorithm

    Adam Machine Learning Algorithm
    2023/08/25
    Adam is like a gardener who knows exactly which tools to use to make sure all of the plants grow evenly and steadily. It's an algorithm that helps optimize training in machine learning by adjusting the learning rate of each weight in the model individually. Imagine you're trying to teach a group of students with different learning abilities and pace. You want to make sure they all learn at a similar rate, but you also want to make sure they're not getting bored waiting for others to catch up. Adam does just that for your machine learning model. Adam is known for its efficiency and low memory requirement, making it a great choice for algorithms that require a lot of iterations and calculations. It achieves this by computing the first-order gradient of the model and keeping track of previous gradient information to adjust the learning rate accordingly. This helps avoid the model getting stuck in local optima (like a car stuck in a rut) and allows it to find the global optimum (like finding the best route to your destination without getting stuck). In a way, Adam helps your model learn more like a human - by adjusting to the individual strengths and weaknesses of each weight and making sure they're all improving at a similar pace. If you're looking for an optimization algorithm that's efficient, quick, and can help your model achieve better results, Adam is a great choice.
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    4 分
  • Adagrad Machine Learning Algorithm

    Adagrad Machine Learning Algorithm
    2023/08/25
    Affinity Propagation, also known as AP, is a machine learning algorithm that helps group similar data points together. It does this by using a "vote" system, where each data point "votes" for other data points it believes are most similar to itself. It's like a big game of telephone, where each person whispers a message to the next person until everyone has heard it. In AP, data points pass messages to each other until they all agree on which data points are best to represent the different clusters. This algorithm is unsupervised, meaning it doesn't need any pre-labeled data. It figures out the optimal number of clusters and which data points belong to each cluster on its own. This can be incredibly helpful to find patterns in your data and make predictions about new data point values. Using Affinity Propagation can make your job easier by quickly and accurately grouping similar data points together without needing any prior knowledge about the data. So next time you're trying to organize a big group of people, think of Affinity Propagation and its "vote" system to help you group people together based on their similarities!
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    5 分

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