エピソード

  • 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
    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
    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
  • Adaboost | The Hitchhiker’s Guide to Machine Learning Algorithms
    2023/07/27

    AdaBoost is a machine learning meta-algorithm that falls under the category of ensemble methods. It can be used in conjunction with many other types of learning algorithms to improve performance. AdaBoost uses supervised learning methods to iteratively train a set of weak classifiers and combine them into a strong classifier. Ever wanted to be listed as a “contributor, editor, or even co-author” on a published book? Now you can! Simply contribute to the Hitchhiker’s Guide to Machine Learning Algorithms ebook by submitting a pull request and you’ll be added! AdaBoost: Introduction Domains: Machine Learning Learning Methods: Supervised Type: Ensemble AdaBoost is a machine learning meta-algorithm that falls under the category of ensemble learning. It is a boosting algorithm, which means it combines multiple weaker models to create a stronger overall model. AdaBoost can be used in conjunction with many other types of learning algorithms to improve their performance, particularly in the realm of supervised learning. The basic idea behind AdaBoost is to iteratively train a sequence of weak classifiers on different subsets of the data. These classifiers are combined into a single strong classifier by assigning weights to each classifier based on its performance. AdaBoost is particularly useful when dealing with high-dimensional datasets, as it can effectively select the most relevant features to improve classification accuracy. In this way, AdaBoost has become a popular and powerful tool in the machine learning community, known for its ability to produce accurate and robust models across a wide range of applications. AdaBoost: Use Cases & Examples AdaBoost is a popular ensemble learning meta-algorithm that can be used in conjunction with many other types of learning algorithms to improve performance. It is a supervised learning method that works by combining several weak learners to create a strong learner. One of the most common use cases of AdaBoost is in object detection, where it is used to identify objects within an image. Another use case is in predicting the likelihood of a customer to churn, which is used in customer retention strategies. AdaBoost has also been used in natural language processing, specifically in sentiment analysis, to classify the sentiment of a given text. It has shown promising results in predicting stock prices and fraud detection as well. Given its versatility, AdaBoost is a powerful tool in the machine learning engineer’s toolkit, and its popularity continues to grow in a variety of industries and applications. AdaBoost: ELI5 AdaBoost, short for Adaptive Boosting, is like a superhero team-up of many machine learning models that work together to fight evil (in this case, inaccuracies in predicting data). Think of it like assembling a team of experts in different fields, each with their unique skills and knowledge. Each expert is assigned a specific task, but they also work together as one to achieve a common goal. Similarly, AdaBoost is a meta-algorithm, meaning it can be paired with a variety of other machine learning algorithms to improve accuracy. It’s like a coach who helps each model improve its weaknesses and work together to make the best prediction possible. AdaBoost is particularly useful in supervised learning, where a model is trained on a labeled dataset to make accurate predictions on new data. By adapting and boosting the performance of each model in the team, AdaBoost can ultimately create a more accurate and reliable prediction than any single model on its own. With AdaBoost in your corner, you can harness the power of multiple models to achieve exceptional results!

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    3 分
  • Introducing Flan-UL2 20B: The Latest Addition to the Open-Source Flan Models
    2023/03/03
    Introducing Flan-UL2 20B: The Latest Addition to the Open-Source Flan Models Researchers have released a new open-source Flan 20B model that was trained on top of the previously open-sourced UL2 20B checkpoint. These checkpoints have been uploaded to Github, and technical details have been updated in the UL2 paper. The Flan series of models are designed to operate on a collection of diverse datasets phrased as instructions for generalisation across multiple tasks. The Flan datasets have now been open-sourced in the "The Flan Collection: Designing Data and Methods for Effective Instruction Tuning" paper (by Longpre et al.). The researchers have also released a series of T5 models ranging from 200M to 11B parameters that have been instruction tuned with Flan in the "Scaling Instruction-Finetuned Language Models (Chung et al.)", also known as the Flan2 paper. What is Flan Instruction Tuning? The key idea of Flan Instruction Tuning is to train a large language model on a collection of datasets phrased as instructions so that the model can generalise across diverse tasks. While Flan has been primarily trained on academic tasks, the researchers are planning to expand the scope of the model to cover other areas in the future. What's New with Flan-UL2 20B? The new Flan-UL2 20B checkpoint has been designed to improve the "usability" of the original UL2 model, which was trained exclusively on the C4 corpus. The UL2 objective trains the model on a mixture of denoisers with diverse span corruption and prefix language modelling tasks. There are two major updates that have been made to the UL2 20B model with Flan. The first update is the use of a receptive field of 2048 instead of the original 512, making it more usable for few-shot in-context learning. The second update is the removal of the mandatory mode switch tokens required for good performance. Instead, the researchers have continued training UL2 for an additional 100k steps (with a small batch) to forget mode tokens before applying Flan instruction tuning. Comparison to Other Models in the Flan Series Flan-UL2 20B outperforms Flan-T5 XXL on all four setups with an overall performance lift of +3.2% relative improvement. The gains on CoT versions of MMLU and BBH tasks have a much larger delta, with an increase of +7.4% for MMLU and +3.1% for BBH when compared to Flan-T5 XXL. Limitations of Flan While Flan models are cost-friendly, compact, and free, there are some limitations associated with these models. For example, Flan is primarily instruction-tuned on academic tasks, where outputs are typically short, academic, and traditional. This limitation means that Flan is mostly useful for academic tasks. Conclusion Flan-UL2 20B is a significant addition to the Flan series of models, as it expands the size ceiling of the current Flan-T5 models by approximately 2x. This new model has been designed to improve the usability of the original UL2 model and exhibits a substantial improvement in CoT capabilities. Researchers are excited to see what the community does with this new model, which is currently the best open-source model on the Big-Bench hard and MMLU.
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    3 分
  • Hey Hey
    2022/11/09

    Devin Schumacher - Hey Hey

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    6 分
  • Just Funkin’ Dance Vol. 3
    2022/10/24

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    44 分
  • Cocktails & Cabanas
    15 分