投稿時間:2023-07-07 01:24:39 RSSフィード2023-07-07 01:00 分まとめ(29件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
IT	気になる、記になる…	Google、公式ストアでサマーセールをスタート ｰ ｢Pixel｣シリーズや｢Pixel Watch｣が対象	https://taisy0.com/2023/07/07/173753.html	google	2023-07-06 15:58:51
IT	気になる、記になる…	YouTube、動画視聴中の誤タップを防止する画面ロック機能をテスト中	https://taisy0.com/2023/07/07/173751.html	premium	2023-07-06 15:45:23
IT	気になる、記になる…	｢Google Pixel Fold｣の分解動画	https://taisy0.com/2023/07/07/173746.html	google	2023-07-06 15:39:24
IT	気になる、記になる…	｢Threads｣の登録者数、初日で3,000万人を突破	https://taisy0.com/2023/07/07/173742.html	threads	2023-07-06 15:03:06
AWS	AWS Big Data Blog	Extract time series from satellite weather data with AWS Lambda	https://aws.amazon.com/blogs/big-data/extract-time-series-from-satellite-weather-data-with-aws-lambda/	Extract time series from satellite weather data with AWS LambdaExtracting time series on given geographical coordinates from satellite or Numerical Weather Prediction data can be challenging because of the volume of data and of its multidimensional nature time latitude longitude height multiple parameters This type of processing can be found in weather and climate research but also in applications like photovoltaic and wind power …	2023-07-06 15:19:31
AWS	AWS The Internet of Things Blog	Industrial Overall Equipment Effectiveness (OEE) guide with AWS IoT SiteWise	https://aws.amazon.com/blogs/iot/industrial-ooee-with-aws-iot-sitewise/	Industrial Overall Equipment Effectiveness OEE guide with AWS IoT SiteWiseIntroduction Overall equipment effectiveness OEE is the standard for measuring manufacturing productivity It encompasses three factors quality performance and availability Therefore a score of OEE would mean a manufacturing system is producing only good parts as fast as possible and with no stop time in other words a perfectly utilized production line OEE provides …	2023-07-06 15:57:37
AWS	AWS Machine Learning Blog	Integrate SaaS platforms with Amazon SageMaker to enable ML-powered applications	https://aws.amazon.com/blogs/machine-learning/integrate-saas-platforms-with-amazon-sagemaker-to-enable-ml-powered-applications/	Integrate SaaS platforms with Amazon SageMaker to enable ML powered applicationsAmazon SageMaker is an end to end machine learning ML platform with wide ranging features to ingest transform and measure bias in data and train deploy and manage models in production with best in class compute and services such as Amazon SageMaker Data Wrangler Amazon SageMaker Studio Amazon SageMaker Canvas Amazon SageMaker Model Registry Amazon SageMaker Feature Store Amazon SageMaker …	2023-07-06 15:17:08
技術ブログ	Developers.IO	Cookiebot の導入を Cloudflare Workers で簡単に行えるツールをローンチしました。	https://dev.classmethod.jp/articles/cookiebot-by-cloudflare-workers/	cloudflareworkers	2023-07-06 15:45:05
技術ブログ	Developers.IO	Amazon Connectで仮想マシンの異常を電話通知してみた	https://dev.classmethod.jp/articles/amazon-connect-onpremises-call/	amazon	2023-07-06 15:00:47
海外TECH	MakeUseOf	How to Build a Wordle Clone With JavaScript	https://www.makeuseof.com/wordle-clone-with-javascript/	javascript	2023-07-06 15:30:19
海外TECH	MakeUseOf	How to Fix the "Windows Could Not Start the Windows Search Service" Error	https://www.makeuseof.com/windows-could-not-start-windows-search-service-fix/	search	2023-07-06 15:16:16
海外TECH	DEV Community	Basics to Machine Learning 🤖	https://dev.to/akashpattnaik/basics-to-machine-learning-3ldk	Basics to Machine Learning Table of Contents Introduction to Machine Learning Types of Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Key Concepts in Machine Learning ️Data Preprocessing Feature Selection and Extraction Model Training and Evaluation ️Popular Machine Learning Algorithms Linear Regression Logistic Regression Decision Trees Random Forests Support Vector Machines ️K Nearest Neighbors Neural Networks Applications of Machine Learning Natural Language Processing ️Image and Video Recognition Fraud Detection ️‍ ️Recommendation Systems Predictive Analytics Challenges and Limitations of Machine Learning ️Data Quality and Quantity Bias and Ethics Interpretability Overfitting and Underfitting ️Future Trends in Machine Learning Conclusion FAQs Introduction to Machine Learning Machine Learning is a branch of Artificial Intelligence AI that focuses on developing algorithms and models that allow computers to learn and make predictions or decisions without being explicitly programmed It is based on the idea that systems can learn from data identify patterns and make intelligent decisions or predictions Types of Machine Learning Supervised Learning Supervised Learning is a type of machine learning where the algorithm learns from labeled data It involves training a model using input output pairs where the desired output is known The model learns to map the inputs to the correct outputs and can then make predictions on new unseen data Unsupervised Learning Unsupervised Learning involves training a model on unlabeled data where the algorithm tries to find patterns or structures in the data without any predefined labels It is used for tasks such as clustering dimensionality reduction and anomaly detection Reinforcement Learning Reinforcement Learning is a type of machine learning where an agent learns to interact with an environment and maximize a reward signal The agent takes actions in the environment and based on the feedback received in the form of rewards or penalties it learns to make better decisions Key Concepts in Machine Learning ️ Data Preprocessing Data Preprocessing is an important step in machine learning where raw data is transformed into a format suitable for analysis It involves tasks such as cleaning the data handling missing values encoding categorical variables and scaling numerical features Feature Selection and Extraction Feature Selection and Extraction involve selecting the most relevant features from the dataset or creating new features that capture important information This helps in reducing the dimensionality of the data and improving the performance of the models Model Training and Evaluation ️Model Training involves feeding the prepared data to a machine learning algorithm to learn patterns and relationships The trained model is thenevaluated using evaluation metrics such as accuracy precision recall and F score to assess its performance Popular Machine Learning Algorithms Linear Regression Linear Regression is a supervised learning algorithm used for predicting a continuous target variable based on one or more input features It fits a straight line to the data by minimizing the sum of the squared differences between the predicted and actual values Logistic Regression Logistic Regression is a supervised learning algorithm used for binary classification tasks It models the relationship between the input variables and the probability of the target variable belonging to a particular class Decision Trees Decision Trees are versatile supervised learning algorithms that can be used for both classification and regression tasks They make decisions by recursively splitting the data based on the values of input features and creating a tree like structure to make predictions Random Forests Random Forests is an ensemble learning algorithm that combines multiple decision trees to improve prediction accuracy It generates a set of decision trees and makes predictions by averaging the predictions of individual trees Support Vector Machines ️Support Vector Machines SVM is a powerful supervised learning algorithm used for classification and regression tasks It separates data points by creating a hyperplane that maximally separates different classes or predicts continuous values K Nearest Neighbors K Nearest Neighbors KNN is a non parametric algorithm used for both classification and regression tasks It predicts the target variable based on the majority vote of its k nearest neighbors in the training data Neural Networks Neural Networks are a class of algorithms inspired by the structure and function of the human brain They consist of interconnected layers of artificial neurons that learn to extract features and make predictions Neural Networks are widely used for various tasks such as image recognition natural language processing and time series forecasting Applications of Machine Learning Natural Language Processing ️Machine Learning has revolutionized natural language processing tasks such as text classification sentiment analysis language translation and chatbot development It enables computers to understand and generate human language leading to advancements in voice assistants and language based applications Image and Video Recognition Machine Learning algorithms particularly deep learning models have greatly improved image and video recognition capabilities They can accurately classify objects detect and track faces identify landmarks and analyze video content for various applications like autonomous vehicles surveillance systems and medical imaging Fraud Detection ️‍ ️Machine Learning is extensively used in fraud detection systems across industries By analyzing patterns and anomalies in large volumes of data machine learning models can identify fraudulent transactions activities or behaviors helping organizations prevent financial losses Recommendation Systems Recommendation systems leverage machine learning algorithms to provide personalized recommendations to users These systems analyze user preferences historical data and behavioral patterns to suggest products movies music and other content enhancing user experience and driving customer engagement Predictive Analytics Machine Learning plays a crucial role in predictive analytics enabling businesses to make data driven forecasts and predictions It helps in areas such as sales forecasting demand planning risk assessment customer churn prediction and market trend analysis Challenges and Limitations of Machine Learning ️ Data Quality and Quantity Machine Learning models heavily rely on the quality and quantity of data Insufficient or biased data can lead to inaccurate predictions or biased outcomes It is crucial to ensure that the training data is representative diverse and free from errors or biases Bias and Ethics Machine Learning algorithms can inherit biases present in the training data leading to unfair ordiscriminatory outcomes It is essential to address issues of bias and ensure ethical considerations in the development and deployment of machine learning models Interpretability Some machine learning algorithms particularly deep learning models are often considered as black boxes making it challenging to interpret how they arrive at their predictions Interpretable machine learning techniques are being developed to provide explanations for the decisions made by these models Overfitting and Underfitting ️Overfitting occurs when a machine learning model performs well on the training data but fails to generalize to unseen data Underfitting on the other hand happens when the model fails to capture the underlying patterns in the data Balancing between these two extremes is crucial to achieve optimal model performance Future Trends in Machine Learning Machine Learning is a rapidly evolving field and several trends are shaping its future Explainable AI There is a growing demand for machine learning models that can provide explanations and justifications for their predictions especially in high stake domains like healthcare and finance Automated Machine Learning Automated Machine Learning AutoML aims to automate the process of model selection feature engineering and hyperparameter tuning making machine learning more accessible to non experts Federated Learning Federated Learning enables training machine learning models on decentralized data sources without the need to centralize sensitive data This approach preserves privacy while allowing for collaborative model training Edge Computing With the rise of Internet of Things IoT devices machine learning models are increasingly being deployed on the edge closer to where data is generated This reduces latency and enhances privacy Continual Learning Continual Learning focuses on developing algorithms that can learn from a continuous stream of data adapt to concept drift and retain knowledge from previous tasks This enables lifelong learning and improves model performance over time Conclusion Machine Learning is a powerful field of study that has revolutionized various industries and applications It enables computers to learn from data and make intelligent predictions or decisions Understanding the basics of machine learning its types key concepts and popular algorithms is essential for anyone interested in this rapidly evolving field As technology continues to advance machine learning is expected to play an even more significant role in shaping our lives and driving innovation across sectors By addressing challenges such as data quality bias and interpretability and embracing future trends we can harness the full potential of machine learning for a better and more intelligent future It took me hours to write this article for newbies Please show love and leave a like and a comment if you liked it Also follow me on github for more such articles FAQs Q What programming languages are commonly used in machine learning A Python is the most widely used programming language for machine learning due to its rich ecosystem of libraries and frameworks such as TensorFlow PyTorch and scikit learn R and Julia are also popular choices among data scientists and researchers Q Can machine learning be used for time series forecasting A Yes machine learning algorithms can be used for time series forecasting tasks Techniques such as ARIMA LSTM and Prophet are commonly employed for predicting future values based on historical time series data Q Is machine learning only applicable to large datasets A Machine learning can be applied to datasets of various sizes from small to large However having sufficient data is crucial for training accurate and robust models The size of the dataset depends on the complexity of the problem and the algorithm being used Q Are there any ethical concerns related to machine learning A Yes machine learning raises ethical concerns such as data privacy bias and transparency It is essential to address these concerns by ensuring data privacy mitigating biases in data and algorithms and providinginterpretability and transparency in model predictions Q How can I get started with machine learning A To get started with machine learning you can begin by learning the fundamentals of programming statistics and mathematics Familiarize yourself with Python and its machine learning libraries Online courses tutorials and practice on real world datasets can also help you gain hands on experience in machine learning Connect with me Mail akashpattnaik github gamil comGithub iAkashPattnaikTwitter akash am	2023-07-06 15:41:09
海外TECH	DEV Community	Code Snippet Builder - Create stunning code snippets	https://dev.to/sachinchaurasiya/code-snippet-builder-create-stunning-code-snippets-1olf	Code Snippet Builder Create stunning code snippetsThe Code Snippet Builder is an innovative tool designed to help developers and programmers create visually appealing and professional looking code snippets for various programming languages With its user friendly interface and drag and drop functionality the builder simplifies the process of showcasing code in an attractive and customizable manner Whether you need to document your code share examples in tutorials or enhance your technical documentation the Code Snippet Builder is the perfect solution It offers a wide range of customizable options including various templates and styling features allowing you to create snippets that align with your branding or project requirements The builder s intuitive drag and drop functionality enables you to effortlessly arrange code blocks add syntax highlighting and format your snippets to make them more visually engaging You can easily customize the appearance of your code snippets adjusting font styles colors background themes and more The Code Snippet Builder supports multiple programming languages catering to the diverse needs of developers across different domains Whether you re working with Python JavaScript TypeScript or any other language you can rely on the builder to generate beautiful code snippets that effectively communicate your ideas Using the Code Snippet Builder you can elevate your code documentation making it more accessible and visually appealing to fellow developers clients or users Start building beautiful code snippets today and enhance how you present and share your code The desktop version of our website provides the best experience For optimal usage please access it from your desktop Tech StackReactJs Empowering the creation of UI components with its component based architecture TypeScript Enabling type safety and preventing runtime errors in the development process Netlify Facilitating the seamless deployment of the project Appwrite CloudAuth Streamlining authentication and authorization management Email PasswordGoogleGithubDatabase Serving as a reliable storage solution for the snippets of data Storage Providing a secure and efficient repository for storing the snapshot images of code snippets Challenges We FacedDuring the development process I encountered some exciting challenges that I had to overcome by reading documentation collaborating with the Appwrite community and seeking help from other developers These particular aspects proved to be the most difficult for me Database relationshipWhen working with the Appwrite cloud I encountered a version mismatch The version I was using x didn t have support for creating relationships between users and their data To tackle this challenge I reached out to the helpful Appwrite community on Discord They provided me with valuable suggestions and feedback Ultimately I decided to associate the user with the snippet data by storing the user ID This solution allowed me to overcome the limitation and establish the desired relationship between users and their data Condition QueryWhen I was working on listing users snippets on the dashboard I came across a challenge I needed to figure out how to fetch only the snippets belonging to the current user After reading the documentation I discovered a solution Appwrite provides a Query model that allows us to write conditions which helped me filter and fetch only the snippets associated with the current user Query equal creator creatorId Snippet Snapshot imageWhile working on the dashboard I encountered a challenge when I wanted to display a visual preview of the snippets created by the user The problem was how to store the snapshot image data To tackle this I referred to the storage documentation and discovered that Appwrite offers a set of methods specifically designed for creating updating and deleting files of various types such as PNG JPG and more This enabled me to successfully store and manage the snapshot images associated with the snippets Public Code RepoThe Code Snippet Builder welcomes contributions and has a public repository available for collaboration Code Snippet Builder is licensed under the MIT License see the LICENSE file for details Demo LinkWebsite Email demo codesnippetbuilder comPassword demo And that s it for this topic Thank you for reading Connect with me LinkedInTwitter	2023-07-06 15:05:59
海外科学	NYT > Science	Heat Records Fall Around the Globe as Earth Warms, Fast	https://www.nytimes.com/2023/07/06/climate/climate-change-record-heat.html	around	2023-07-06 15:46:59
金融	◇◇　保険デイリーニュース　◇◇（損保担当者必携！）	保険デイリーニュース(07/07)	http://www.yanaharu.com/ins/?p=5251	sompo	2023-07-06 15:39:00
金融	金融庁ホームページ	バーゼル銀行監督委員会による「信用リスクに関するニューズレター」の公表について掲載しました。	https://www.fsa.go.jp/inter/bis/20230706/20230706.html	信用リスク	2023-07-06 17:00:00
金融	ニュース - 保険市場TIMES	ペット＆ファミリー、「げんきナンバーわんスリム」のキャンペーン実施	https://www.hokende.com/news/blog/entry/2023/07/07/010000	ペットファミリー、「げんきナンバーわんスリム」のキャンペーン実施ウェブ申し込み限定ペットファミリー損害保険株式会社は月日、同社が販売している「げんきナンバーわんスリム」の新規申し込みキャンペーンを実施していると発表した。	2023-07-07 01:00:00
ニュース	BBC News - Home	Elle Edwards: Connor Chapman guilty of Christmas Eve pub murder	https://www.bbc.co.uk/news/uk-england-merseyside-66108449?at_medium=RSS&at_campaign=KARANGA	chapman	2023-07-06 15:55:16
ニュース	BBC News - Home	Threads: Thirty million join Meta's Twitter rival, Zuckerberg says	https://www.bbc.co.uk/news/technology-66112648?at_medium=RSS&at_campaign=KARANGA	twitter	2023-07-06 15:30:07
ニュース	BBC News - Home	Mothers could have missed out on £1bn in state pension	https://www.bbc.co.uk/news/business-66124840?at_medium=RSS&at_campaign=KARANGA	pension	2023-07-06 15:48:39
ニュース	BBC News - Home	Where is Yevgeny Prigozhin? And why does it matter?	https://www.bbc.co.uk/news/world-europe-66126005?at_medium=RSS&at_campaign=KARANGA	group	2023-07-06 15:17:39
ニュース	BBC News - Home	Taunton doctor who put bodily fluid in coffee sentenced	https://www.bbc.co.uk/news/uk-england-somerset-66110697?at_medium=RSS&at_campaign=KARANGA	community	2023-07-06 15:10:01
ニュース	BBC News - Home	Man goes on trial accused of strangling sister to death	https://www.bbc.co.uk/news/uk-scotland-glasgow-west-66120042?at_medium=RSS&at_campaign=KARANGA	hamilton	2023-07-06 15:25:04
ニュース	BBC News - Home	Currys boss: Smart speaker sales have fallen off a cliff	https://www.bbc.co.uk/news/66120000?at_medium=RSS&at_campaign=KARANGA	customers	2023-07-06 15:04:43
ニュース	BBC News - Home	Ashes: Mark Wood removes Australia's Starc and Pat Cummins in three balls	https://www.bbc.co.uk/sport/av/cricket/66126965?at_medium=RSS&at_campaign=KARANGA	Ashes Mark Wood removes Australia x s Starc and Pat Cummins in three ballsEngland fast bowler Mark Wood bowls Mitchell Starc for two and traps Australia captain Pat Cummins lbw for a duck within the space of three balls on day one of the third Ashes Test	2023-07-06 15:44:35
ニュース	BBC News - Home	Anthony Joshua v Dillian Whyte: Heavyweight rematch to take place on 12 August in London	https://www.bbc.co.uk/sport/boxing/66081393?at_medium=RSS&at_campaign=KARANGA	Anthony Joshua v Dillian Whyte Heavyweight rematch to take place on August in LondonHeavyweight Anthony Joshua will face fellow Briton Dillian Whyte at London s O Arena on August a rematch of their bout	2023-07-06 15:35:15
ニュース	BBC News - Home	Wimbledon 2023 results: Katie Boulter through, Jan Choinski out	https://www.bbc.co.uk/sport/tennis/66115993?at_medium=RSS&at_campaign=KARANGA	straight	2023-07-06 15:48:05
ニュース	BBC News - Home	Tour de France: Tadej Pogacar beats Jonas Vingegaard to win stage six after thrilling finale	https://www.bbc.co.uk/sport/cycling/66126377?at_medium=RSS&at_campaign=KARANGA	Tour de France Tadej Pogacar beats Jonas Vingegaard to win stage six after thrilling finaleTadej Pogacar produces a superb late attack to power away from defending champion Jonas Vingegaard and win stage six of the Tour de France	2023-07-06 15:56:20
ニュース	BBC News - Home	The Ashes 2023: England's Joe Root drops Alex Carey then catches Travis Head in consecutive balls	https://www.bbc.co.uk/sport/av/cricket/66120902?at_medium=RSS&at_campaign=KARANGA	The Ashes England x s Joe Root drops Alex Carey then catches Travis Head in consecutive ballsWatch as England s Joe Root catches Australia s Travis Head moments after dropping Alex Carey on four on a thrilling first day of the third Ashes Test	2023-07-06 15:49:25