Im ersten Teil unserer Serie „Buzzword Bingo: Data Science“ widmeten wir uns den Begriffen Künstliche Intelligenz, Algorithmen und Maschinelles Lernen. Nun geht es hier im zweiten Teil weiter mit der Begriffsklärung dreier weiterer Begriffe aus dem Data Science-Umfeld.
https://data-science-blog.com/wp-content/uploads/2022/07/Buzzword-Bingo-Data-Science-Teil-II_800x384.jpg384800Julius Meierhttps://www.data-science-blog.com/wp-content/uploads/2016/09/data-science-blog-logo.pngJulius Meier2022-07-08 11:07:522022-07-08 11:07:52Buzzword Bingo: Data Science – Teil II
Already familiar with the term big data, right? Despite the fact that we would all discuss Big Data, it takes a very long time before you confront it in your career. Apache Spark is a Big Data tool that aims to handle large datasets in a parallel and distributed manner. Apache Spark began as a research project at UC Berkeley’s AMPLab, a student, researcher, and faculty collaboration centered on data-intensive application domains, in 2009.
Some of you might have got away with explaining reinforcement learning (RL) only by saying an obscure thing like “RL enables computers to learn through trial and errors.” But if you have patiently read my articles so far, you might have come to say “RL is a family of algorithms which simulate procedures similar to dynamic programming (DP).”
https://data-science-blog.com/wp-content/uploads/2022/06/RL_head_image_2.png383935Yasuto Tamurahttps://www.data-science-blog.com/wp-content/uploads/2016/09/data-science-blog-logo.pngYasuto Tamura2022-07-01 10:15:312022-06-30 22:48:57Stop saying “trial and errors” for now: seeing reinforcement learning through some spectrums
Image Source: source unsplash.com Downtime for a data center can be extraordinarily costly — potentially leading to lost revenue, lost customers and a damaged reputation. Preventative maintenance (PM) helps keep […]
https://data-science-blog.com/wp-content/uploads/2022/03/server-cupboard-gitter.png5531043Shannon Flynnhttps://www.data-science-blog.com/wp-content/uploads/2016/09/data-science-blog-logo.pngShannon Flynn2022-06-28 08:25:042022-06-29 09:58:403 Types of Preventative Maintenance for Data Centers
In deep learning, there are different training methods. Which one we use in an AI project depends on the data provided by our customer: how much data is there, is it labeled or unlabeled? Or is there both labeled and unlabeled data?
https://data-science-blog.com/wp-content/uploads/2022/05/training-of-ai-models.jpg8002106Benjamin Aunkoferhttps://www.data-science-blog.com/wp-content/uploads/2016/09/data-science-blog-logo.pngBenjamin Aunkofer2022-06-20 11:18:562022-05-20 11:20:03It’s All About Data: The Training of AI Models
https://data-science-blog.com/wp-content/uploads/2022/06/Buzzword-Bingo-I.png404841Julius Meierhttps://www.data-science-blog.com/wp-content/uploads/2016/09/data-science-blog-logo.pngJulius Meier2022-06-16 10:29:272022-06-15 21:32:27Buzzword Bingo: Data Science – Teil I
Nach dutzenden Process Mining Projekten mit unterschiedlichen Rahmenbedingungen gebe ich hier nun sechs handfeste Hinweise, wie Process Mining Projekte generell zum Erfolg werden.
https://data-science-blog.com/wp-content/uploads/2022/06/process-mining-process.png16452575Benjamin Aunkoferhttps://www.data-science-blog.com/wp-content/uploads/2016/09/data-science-blog-logo.pngBenjamin Aunkofer2022-06-13 08:55:562022-06-14 07:56:576 Faktoren, wie Process Mining Projekte zum Erfolg werden
This article focuses on autonomous trading agent to solve the capital market portfolio management problem. Researchers aim to achieve higher portfolio return while preferring lower-risk actions. It uses deep reinforcement learning Deep Q-Network (DQN) to train the agent.
Natural Language Understanding (NLU) ist ein Teilbereich von Computer Science, der sich damit beschäftigt natürliche Sprache, also beispielsweise Texte oder Sprachaufnahmen, verstehen und verarbeiten zu können. Das Ziel ist es, dass eine Maschine in der gleichen Weise mit Menschen kommunizieren kann, wie es Menschen untereinander bereits seit Jahrhunderten tun.
Fraud has the potential to shatter businesses of all sizes and in all industries. Now that many businesses operate online at least partially, if not completely, the fraud risks are […]
Buzzword Bingo: Data Science – Teil II
/in Artificial Intelligence, Big Data, Business Analytics, Data Engineering, Data Mining, Data Science, Deep Learning, Insights, Machine Learning, Main Category/by Julius MeierIm ersten Teil unserer Serie „Buzzword Bingo: Data Science“ widmeten wir uns den Begriffen Künstliche Intelligenz, Algorithmen und Maschinelles Lernen. Nun geht es hier im zweiten Teil weiter mit der Begriffsklärung dreier weiterer Begriffe aus dem Data Science-Umfeld.
5 Apache Spark Best Practices
/in Big Data, Data Science Hack, Tool Introduction, Tools/by Sai PriyaAlready familiar with the term big data, right? Despite the fact that we would all discuss Big Data, it takes a very long time before you confront it in your career. Apache Spark is a Big Data tool that aims to handle large datasets in a parallel and distributed manner. Apache Spark began as a research project at UC Berkeley’s AMPLab, a student, researcher, and faculty collaboration centered on data-intensive application domains, in 2009.
Stop saying “trial and errors” for now: seeing reinforcement learning through some spectrums
/in Artificial Intelligence, Data Science, Deep Learning/by Yasuto TamuraSome of you might have got away with explaining reinforcement learning (RL) only by saying an obscure thing like “RL enables computers to learn through trial and errors.” But if you have patiently read my articles so far, you might have come to say “RL is a family of algorithms which simulate procedures similar to dynamic programming (DP).”
3 Types of Preventative Maintenance for Data Centers
/in Predictive Analytics/by Shannon FlynnImage Source: source unsplash.com Downtime for a data center can be extraordinarily costly — potentially leading to lost revenue, lost customers and a damaged reputation. Preventative maintenance (PM) helps keep […]
It’s All About Data: The Training of AI Models
/in Artificial Intelligence, Data Science, Deep Learning, Machine Learning, Main Category/by Benjamin AunkoferIn deep learning, there are different training methods. Which one we use in an AI project depends on the data provided by our customer: how much data is there, is it labeled or unlabeled? Or is there both labeled and unlabeled data?
Buzzword Bingo: Data Science – Teil I
/in Artificial Intelligence, Data Mining, Data Science, Deep Learning, Machine Learning, Main Category/by Julius MeierDer im Bereich der Data Science u. a. am häufigsten genutzte Begriff ist derjenige der „Künstlichen Intelligenz“.
6 Faktoren, wie Process Mining Projekte zum Erfolg werden
/in Business Analytics, Business Intelligence, Insights, Main Category, Process Mining/by Benjamin AunkoferNach dutzenden Process Mining Projekten mit unterschiedlichen Rahmenbedingungen gebe ich hier nun sechs handfeste Hinweise, wie Process Mining Projekte generell zum Erfolg werden.
Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning
/in Artificial Intelligence, Data Science, Deep Learning, Machine Learning, Main Category/by Jieyun HuThis article focuses on autonomous trading agent to solve the capital market portfolio management problem. Researchers aim to achieve higher portfolio return while preferring lower-risk actions. It uses deep reinforcement learning Deep Q-Network (DQN) to train the agent.
Wie Maschinen uns verstehen: Natural Language Understanding
/in Artificial Intelligence, Data Mining, Data Science, Deep Learning, Insights, Machine Learning, Main Category, Natural Language Processing/by Niklas LangNatural Language Understanding (NLU) ist ein Teilbereich von Computer Science, der sich damit beschäftigt natürliche Sprache, also beispielsweise Texte oder Sprachaufnahmen, verstehen und verarbeiten zu können. Das Ziel ist es, dass eine Maschine in der gleichen Weise mit Menschen kommunizieren kann, wie es Menschen untereinander bereits seit Jahrhunderten tun.
How Online Businesses Can Mitigate Fraud Risk
/in Insights/by Nadica MetulevaFraud has the potential to shatter businesses of all sizes and in all industries. Now that many businesses operate online at least partially, if not completely, the fraud risks are […]