投稿時間:2023-01-28 18:15:19 RSSフィード2023-01-28 18:00 分まとめ(17件)

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python	Pythonタグが付けられた新着投稿 - Qiita	プログラムにページャ機能を付ける	https://qiita.com/tadashi9e/items/dd9bd247163c468c6fce	表示	2023-01-28 17:47:16
python	Pythonタグが付けられた新着投稿 - Qiita	Transformerの基本	https://qiita.com/DaigakuinnseiNo/items/77deb45140c2d5d1aa35	transformer	2023-01-28 17:10:14
js	JavaScriptタグが付けられた新着投稿 - Qiita	js-cookieの使い方【備忘録】	https://qiita.com/yuuki-h/items/dbfbc85a79b7e21e81fd	jscookie	2023-01-28 17:09:09
Ruby	Rubyタグが付けられた新着投稿 - Qiita	&. （ぼっち演算子）	https://qiita.com/ppi/items/b1bf844f3c3db3e12d45	結果	2023-01-28 17:57:59
Ruby	Rubyタグが付けられた新着投稿 - Qiita	rails db:create 違うdevelopment生成	https://qiita.com/nktyn_frtn0906/items/ed503d52a995bdceb0f5	httpsprogra	2023-01-28 17:19:23
AWS	AWSタグが付けられた新着投稿 - Qiita	【AWS】ELBのアクセスログを有効にする方法。S3バケットポリシーの編集で簡単2ステップで実施可能！	https://qiita.com/Ryo-0131/items/b81d7312136e8ca4118a	設定	2023-01-28 17:10:51
GCP	gcpタグが付けられた新着投稿 - Qiita	GCP - Cloud Digital Leader 試験対策（用語整理）	https://qiita.com/Atsulabo/items/9d18f8fbf7bd1f0ffec8	aiapi	2023-01-28 17:24:50
Ruby	Railsタグが付けられた新着投稿 - Qiita	&. （ぼっち演算子）	https://qiita.com/ppi/items/b1bf844f3c3db3e12d45	結果	2023-01-28 17:57:59
Ruby	Railsタグが付けられた新着投稿 - Qiita	rails db:create 違うdevelopment生成	https://qiita.com/nktyn_frtn0906/items/ed503d52a995bdceb0f5	httpsprogra	2023-01-28 17:19:23
技術ブログ	Developers.IO	[アップデート] AWS Fault Injection Simulator で EBS の I/O 停止アクションが利用出来るようになりました	https://dev.classmethod.jp/articles/aws-fis-pause-io-action-ebs/	faultinjectionsimulator	2023-01-28 08:55:27
海外TECH	DEV Community	10 Techniques for Improving Machine Learning Models	https://dev.to/anurag629/10-techniques-for-improving-machine-learning-models-18k9	Techniques for Improving Machine Learning ModelsHeuristic search is a method of problem solving that uses a specific set of rules or heuristics to guide the search for a solution In inductive learning the heuristic search can be used to search for the most likely hypothesis or model that explains a given set of data This can be done by using heuristics to guide the search through the space of possible hypotheses and evaluating each hypothesis based on how well it fits the data Heuristic search can be useful in inductive learning because it can help to find a good hypothesis quickly even when the space of possible hypotheses is large and complex There are several techniques that can be used to optimize the complexity of a hypothesis during a heuristic search in inductive learning Occam s Razor This principle states that given a set of competing hypotheses the simplest hypothesis that explains the data is the most likely to be true This can be used to guide the search by favoring simpler hypotheses over more complex ones For example imagine you are trying to explain why a certain plant is not growing well in your garden You might come up with several hypotheses such as Hypothesis The plant is not getting enough water Hypothesis The plant is not getting enough sunlight Hypothesis The plant has a disease that is causing it to not grow well Hypothesis The plant is not getting enough water and sunlight and it also has a disease According to Occam s Razor the simplest hypothesis in this case Hypothesis or is the most likely to be true because it explains the data the plant not growing well with the least amount of assumptions Therefore in this example the solution would be to make sure the plant is getting enough water or sunlight before moving to more complex explanations Regularization This technique adds a penalty term to the likelihood of a hypothesis that is proportional to its complexity This can help to discourage overly complex hypotheses and encourage simpler ones For example imagine you are trying to build a machine learning model that predicts the price of a house based on various features such as square footage number of bedrooms etc If you have a lot of features and a complex model it might fit the data very well but have high complexity This could lead to overfitting where the model performs well on the training data but poorly on unseen data In this case you could use regularization to add a penalty term to the likelihood of the hypothesis that is proportional to its complexity This would discourage overly complex models and encourage simpler models that generalize better to unseen data A common method of regularization is L and L regularization which add a penalty term to the sum of the absolute values or squares of the parameters respectively In simple terms regularization can be thought of as a way to keep the model simple and prevent it from overfitting the training data It helps to find the balance between fitting the data well and keeping the model simple Pruning This technique involves removing hypotheses that are unlikely to be true based on their complexity This can be done by setting a maximum complexity threshold or by using other heuristics to identify and eliminate complex hypotheses that are unlikely to be true For example imagine you are trying to build a decision tree for classifying animals The decision tree starts with the root node and branches off into different sub nodes depending on the value of certain features As you keep adding sub nodes the decision tree becomes more complex However not all of these sub nodes are necessary to classify the animals correctly Some of them might be overfitting the data and not providing any useful information In this case you could use pruning to remove these unnecessary sub nodes and simplify the decision tree One popular method of pruning is reduced error pruning where the accuracy of the decision tree is computed on a validation dataset after each node is pruned and if the accuracy does not decrease the node is removed In simple terms pruning can be thought of as a way to simplify the model by removing unnecessary parts of it This can help to improve the performance of the model and make it more interpretable Ensemble methods Ensemble methods involve combining multiple hypotheses to form a more robust and accurate final hypothesis This can be done by averaging the predictions of different models or by combining them in other ways For example imagine you are trying to predict the stock market prices You might use different models such as linear regression decision trees and random forest Each of these models will make predictions based on different features and might have different strengths and weaknesses An ensemble method such as bagging or boosting could be used to combine the predictions of these models in order to form a more robust and accurate final prediction Bagging is used to decrease the variance of predictions by training multiple models independently and averaging them Boosting is used to decrease the bias of predictions by training multiple models sequentially and giving more weight to the misclassified examples In simple terms ensemble methods can be thought of as a way to improve the performance of a model by combining the predictions of multiple models This can help to reduce the errors of individual models and create a more robust and accurate final hypothesis Cross validation This technique involves splitting the data into training and testing sets and evaluating the performance of a hypothesis on the testing set This can be used to identify hypotheses that are overfitting the data and are likely to be overly complex For example imagine you are trying to build a machine learning model to classify an email as spam or not spam You have a dataset of emails and you want to use of the data for training and for testing In traditional validation you would randomly split the data into a training set and a testing set and train the model on the training set and test it on the testing set But this method may lead to overfitting if the testing set is not representative Instead you can use cross validation where the data is split into k folds and the model is trained and tested k times each time with a different fold as the testing set This way all the data is used for testing and training and it provides a more robust estimate of the model s performance In simple terms cross validation is a method of evaluating the performance of a hypothesis by testing it on multiple subsets of data This can help to identify the best hypothesis and avoid overfitting by providing an unbiased estimate of the model performance Bayesian Model Selection Bayesian Model Selection is a method of comparing the relative likelihoods of different hypotheses given the data and selecting the hypothesis with the highest likelihood This can help to identify the best hypothesis and avoid overfitting by taking into account the complexity of the hypothesis For example imagine you are trying to build a machine learning model to predict the price of a house based on certain features such as square footage and the number of bedrooms You want to compare different linear regression models with different numbers of features such as a simple model with only square footage as a feature and a more complex model with square footage and the number of bedrooms as features In traditional model selection you would simply choose the model with the lowest error on the training data But this method may lead to overfitting as the model with more features may have a lower error on the training data but a higher error on the testing data Instead you can use Bayesian Model Selection where you calculate the relative likelihood of each model given the data and select the model with the highest likelihood This way it takes into account the complexity of the model and the amount of data available In simple terms Bayesian Model Selection is a method of comparing the relative likelihoods of different hypotheses given the data and selecting the hypothesis with the highest likelihood This can help to identify the best hypothesis and avoid overfitting by taking into account the complexity of the hypothesis Genetic Algorithm This is an optimization technique that uses principles of natural selection to find the optimal solution It can be used to search through the space of possible hypotheses and evolve the best hypothesis over time For example imagine you are trying to build a machine learning model to predict stock prices You have a set of parameters that influence the performance of the model such as learning rate number of layers and number of neurons A genetic algorithm can be used to find the best combination of these parameters that results in the highest accuracy for the model The genetic algorithm starts by generating a population of random solutions parameter combinations and then evaluates the fitness of each solution model accuracy The best solutions models are then selected and used to create a new population through a process of crossover and mutation This process is repeated multiple times until a satisfactory solution is found In simple terms Genetic Algorithm is a method of optimization inspired by the process of natural selection in biology that helps to find the best combination of parameters that results in the highest accuracy for the model by simulating the process of evolution Particle Swarm Optimization This is another optimization technique that can be used to search through the space of possible hypotheses It is based on the behavior of swarms of particles which move toward the best solution through a process of trial and error For example imagine you are trying to build a machine learning model to predict stock prices You have a set of parameters that influence the performance of the model such as learning rate number of layers and number of neurons A PSO algorithm can be used to find the best combination of these parameters that results in the highest accuracy for the model The PSO algorithm starts by generating a population of particles parameter combinations and then evaluates the fitness of each particle model accuracy Each particle then moves towards the best solution it has encountered so far as well as the best solution encountered by the entire swarm This process is repeated multiple times until a satisfactory solution is found In simple terms Particle Swarm Optimization PSO is a method of optimization inspired by the behavior of a swarm of particles such as birds or fish that helps to find the best combination of parameters that results in the highest accuracy for the model by simulating the movement of a swarm of particles Randomized search Randomized search is a method of optimization that randomly samples from the set of possible solutions to find the best one For example imagine you are trying to build a machine learning model to predict stock prices You have a set of parameters that influence the performance of the model such as learning rate number of layers and number of neurons A randomized search can be used to find the best combination of these parameters that results in the highest accuracy for the model The randomized search algorithm starts by generating a set of random parameter combinations and then evaluates the fitness of each combination model accuracy The best combination is then selected as the solution The process is repeated a number of times with different random parameter combinations until a satisfactory solution is found In simple terms Randomized search is a method of optimization that randomly samples from the set of possible solutions to find the best one It can be used to find the best combination of parameters that results in the highest accuracy for the model Hill Climbing Algorithm Hill Climbing is a method of optimization that iteratively improves a solution by making small changes to it and evaluating whether the new solution is better than the previous one For example imagine you are trying to build a machine learning model to predict stock prices You have a set of parameters that influence the performance of the model such as learning rate number of layers and number of neurons A Hill Climbing algorithm can be used to find the best combination of these parameters that results in the highest accuracy for the model The Hill Climbing algorithm starts by selecting an initial solution a set of parameters and then evaluates the fitness of this solution model accuracy It then makes small changes to the solution and evaluates the new solution If the new solution is better than the previous one it becomes the new current solution This process is repeated until a satisfactory solution is found In simple terms Hill Climbing Algorithm is a method of optimization that iteratively improves a solution by making small changes to it and evaluating whether the new solution is better than the previous one It can be used to find the best combination of parameters that results in the highest accuracy for the model GitHub link Complete Data Science Bootcamp Main Post Complete Data Science Bootcamp	2023-01-28 08:45:30
海外TECH	DEV Community	Introduction to UTXO vs Account-Based Model	https://dev.to/zt4ff_1/introduction-to-utxo-vs-account-based-model-56cc	Introduction to UTXO vs Account Based ModelOne of the importance of money is transferability In Blockchain transactions occur to change the state of nodes on the network For instance if Alice owns BTC and Bob owns BTC Alice can sign a transaction to send BTC to Bob The resulting transaction would update the state of BTC accrued to Alice to BTC while Bob s to BTC There are two major types of recording keeping available in today s Blockchain network Blockchain at its core is simply a giant shared ledger These models define how the state of nodes on the network is being kept There s more to the concept when you start to consider how the general state of the network is managed This article tries to explain these concepts as simply as possible What is the UTXO ModelUTXO stands for Unspent Transaction Output It is a model used by Bitcoin In this model you need an input which references every unspent transaction and an output which references the address you are sending to It can be compared to cash if Alice has a note it can be said that Alice has a UTXO of If she has a couple of smaller notes making up the balance of those smaller notes are individual UTXOs making up the total balance The UTXO keeps track of the notes rather than the balance available to the user If Alice wants to send to Bob the smaller note she has that makes up the would act as the input and new notes UTXO making up and is paid to Alice and Bob respectively Let s consider the diagram below as an example Alice owns two unspent transaction outputs of each if Alice wants to send to Bob both inputs are combined into the transaction and then new outputs are generated This model does not keep the balance of users Wallets have a way to find transactions linked to your address and compute the amount of unspent transaction output as the user balance What is the Account ModelThe Account Model keeps track of balances within accounts Ethereum uses this model Transactions in this model trigger an operation to reduce the balance of the sender while increasing the balance of the receiver This model compares closely with the traditional banking system The bank keeps a state of your balance If you want to withdraw some cash at an ATM it won t matter the kind of notes you deposited with them You will be able to withdraw any combination of notes available to the ATM provided that your available balance is greater than the amount to be withdrawn Let s consider the diagram below as an example Alice has a balance of making up her account irrespective of the notes deposited by her She sent to Bob which reduced her balance by and increase Bob s balance in turn ConclusionWe have seen the basic difference between UTXO and the Account based model Account based model is a simple concept compared to the UTXO model but each model offers its pros and cons which can be considered when designing a blockchain network To learn more I recommend going through this resource and trying to create a mock of this concept using any programming language you are comfortable with	2023-01-28 08:29:50
海外ニュース	Japan Times latest articles	Japanese pair each fall short in their Australian Open wheelchair finals	https://www.japantimes.co.jp/sports/2023/01/28/tennis/japanese-pair-wheelchair-finals/	Japanese pair each fall short in their Australian Open wheelchair finalsTokito Oda the heir apparent to all time great Shingo Kunieda lost in the men s singles final while Yui Kamiji lost a three set affair in	2023-01-28 17:25:13
ニュース	BBC News - Home	Jerusalem synagogue attack: Seven killed in shooting	https://www.bbc.co.uk/news/world-middle-east-64430491?at_medium=RSS&at_campaign=KARANGA	jerusalem	2023-01-28 08:08:42
ニュース	BBC News - Home	Flybe: Regional carrier ceases trading and cancels all flights	https://www.bbc.co.uk/news/uk-64436500?at_medium=RSS&at_campaign=KARANGA	airline	2023-01-28 08:31:42
ビジネス	プレジデントオンライン	新聞は｢異例の会見｣と報じたが…トヨタの社長交代会見での最重要発言｢町いちばんのクルマ屋｣の真意 - 豊田章男社長が2年前から繰り返し説いていたこと	https://president.jp/articles/-/65974	豊田章男	2023-01-28 18:00:00
海外TECH	reddit	T1 vs. Dplus KIA / LCK 2023 Spring - Week 2 / Post-Match Discussion	https://www.reddit.com/r/leagueoflegends/comments/10na81t/t1_vs_dplus_kia_lck_2023_spring_week_2_postmatch/	T vs Dplus KIA LCK Spring Week Post Match DiscussionLCK SPRING Official page Leaguepedia Liquipedia Eventvods com New to LoL T Dplus KIA T Leaguepedia Liquipedia Website Twitter Facebook YouTube DK Leaguepedia Liquipedia Website Twitter Facebook YouTube MATCH T vs DK Winner Dplus KIA in m POG Canyon Damage Graph Runes Bans Bans G K T D B T maokai ryze varus kassadin ksante k C H B O DK ashe elise caitlyn renekton jayce k H HT O B O T vs DK Zeus yone TOP gnar Canna Oner wukong JNG sejuani Canyon Faker azir MID taliyah ShowMaker Gumayusi lucian BOT zeri Deft Keria nami SUP yuumi Kellin MATCH T vs DK Winner T in m POG Oner Damage Graph Runes Bans Bans G K T D B T maokai ryze varus kassadin taliyah k C H M M B DK ashe elise caitlyn jayce gnar k H O M T vs DK Zeus jax TOP ksante Canna Oner vi JNG sejuani Canyon Faker azir MID akali ShowMaker Gumayusi lucian BOT zeri Deft Keria nami SUP yuumi Kellin MATCH DK vs T Winner T in m POG Faker Damage Graph Runes Bans Bans G K T D B DK ashe varus galio sivir ezreal k H H T lucian caitlyn yuumi elise vi k CT I O O B DK vs T Canna ornn TOP jayce Zeus Canyon graves JNG sejuani Oner ShowMaker ryze MID kassadin Faker Deft zeri BOT draven Gumayusi Kellin lulu SUP kalista Keria Patch This thread was created by the Post Match Team We are looking for volunteers to help out with Post Match Threads Please send a message to reddit user lolpmtc with your email address to join submitted by u adzr to r leagueoflegends link comments	2023-01-28 08:31:31