Listing 1 - 10 of 11 | << page >> |
Sort by
|
Choose an application
Choose an application
Choose an application
Kaartspel --- Browser --- Computerspel --- Multimediaproductie
Choose an application
Long description: Biographical note: Hans-Georg Schumann war Informatik- und Mathematiklehrer an einer Gesamtschule. Er hat viele erfolgreiche Bücher in der mitp-Buchreihe »... für Kids« geschrieben.
Computerspiele --- Spiele --- Windows --- Programmierung --- Web --- JavaScript --- Webprogrammierung --- programmieren --- Spieleprogrammierung --- Grafik --- HTML --- Website --- Webseite --- browser --- Buc55
Choose an application
Biographical note: Long description:
Office --- Cloud --- Textverarbeitung --- Word --- buch --- Homeoffice --- Online --- Büroalltag --- Office 365 --- browser --- OneDrive --- zusammenarbeiten --- mitp --- microsoft word --- office buch --- office lens --- word app --- word buch --- word office ms word --- word online
Choose an application
Information systems --- DARPA Internet --- Internet --- Internet (Computer network) --- Internet (Computernetwerk) --- Internet (Réseau d'ordinateurs) --- World Wide Web --- World Wide Web (Informatie-retrieval systeem) --- World Wide Web (Information retrieval system ) --- World Wide Web (Système de récupération des informations) --- internet --- world wide web --- browser (webbrowser) --- tcp/ip --- html --- (zie ook: zoekmachine)
Choose an application
Programming --- HTML (hypertext markup language) --- Computer architecture. Operating systems --- Java (Computer program language) --- World Wide Web --- World Wide web --- 681.3*D32 --- 681.3*D3 --- W3 (World Wide Web) --- Web (World Wide Web) --- World Wide Web (Information retrieval system) --- WWW (World Wide Web) --- Hypertext systems --- Multimedia systems --- Internet --- Object-oriented programming languages --- JavaSpaces technology --- language classifications: applicative languages; data-flow languages; design languages; extensible languages; macro and assembly languages; nonprocedural languages; specialized application and very high-level languages (Programminglanguages) --- Programming languages --- World Wide Web, --- 681.3*D3 Programming languages --- 681.3*D32 language classifications: applicative languages; data-flow languages; design languages; extensible languages; macro and assembly languages; nonprocedural languages; specialized application and very high-level languages (Programminglanguages) --- Java (Computer program language). --- World Wide Web. --- World Wide Web,. --- JAVA --- INFORMATICS --- WWW --- Animation --- NETWORKS --- HTML --- Interactivity --- Web browser --- HotJava --- Monograph --- Computer science. --- Informatics --- Science
Choose an application
Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields.
fintech --- financial technology --- blockchain --- deep learning --- regtech --- environment --- social sciences --- machine learning --- learning analytics --- student field forecasting --- imbalanced datasets --- explainable machine learning --- intelligent tutoring system --- adversarial machine learning --- transfer learning --- cognitive bias --- stock market --- behavioural finance --- investor’s profile --- Teheran Stock Exchange --- unsupervised learning --- clustering --- big data frameworks --- fault tolerance --- stream processing systems --- distributed frameworks --- Spark --- Hadoop --- Storm --- Samza --- Flink --- comparative analysis --- a survey --- data science --- educational data mining --- supervised learning --- secondary education --- academic performance --- text-to-SQL --- natural language processing --- database --- machine translation --- medical image segmentation --- convolutional neural networks --- SE block --- U-net --- DeepLabV3plus --- cyber-security --- medical services --- cyber-attacks --- data communication --- distributed ledger --- identity management --- RAFT --- HL7 --- electronic health record --- Hyperledger Composer --- cybersecurity --- password security --- browser security --- social media --- ANOVA --- SPSS --- internet of things --- cloud computing --- computational models --- metaheuristics --- phishing detection --- website phishing
Choose an application
Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields.
fintech --- financial technology --- blockchain --- deep learning --- regtech --- environment --- social sciences --- machine learning --- learning analytics --- student field forecasting --- imbalanced datasets --- explainable machine learning --- intelligent tutoring system --- adversarial machine learning --- transfer learning --- cognitive bias --- stock market --- behavioural finance --- investor’s profile --- Teheran Stock Exchange --- unsupervised learning --- clustering --- big data frameworks --- fault tolerance --- stream processing systems --- distributed frameworks --- Spark --- Hadoop --- Storm --- Samza --- Flink --- comparative analysis --- a survey --- data science --- educational data mining --- supervised learning --- secondary education --- academic performance --- text-to-SQL --- natural language processing --- database --- machine translation --- medical image segmentation --- convolutional neural networks --- SE block --- U-net --- DeepLabV3plus --- cyber-security --- medical services --- cyber-attacks --- data communication --- distributed ledger --- identity management --- RAFT --- HL7 --- electronic health record --- Hyperledger Composer --- cybersecurity --- password security --- browser security --- social media --- ANOVA --- SPSS --- internet of things --- cloud computing --- computational models --- metaheuristics --- phishing detection --- website phishing
Choose an application
Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields.
fintech --- financial technology --- blockchain --- deep learning --- regtech --- environment --- social sciences --- machine learning --- learning analytics --- student field forecasting --- imbalanced datasets --- explainable machine learning --- intelligent tutoring system --- adversarial machine learning --- transfer learning --- cognitive bias --- stock market --- behavioural finance --- investor’s profile --- Teheran Stock Exchange --- unsupervised learning --- clustering --- big data frameworks --- fault tolerance --- stream processing systems --- distributed frameworks --- Spark --- Hadoop --- Storm --- Samza --- Flink --- comparative analysis --- a survey --- data science --- educational data mining --- supervised learning --- secondary education --- academic performance --- text-to-SQL --- natural language processing --- database --- machine translation --- medical image segmentation --- convolutional neural networks --- SE block --- U-net --- DeepLabV3plus --- cyber-security --- medical services --- cyber-attacks --- data communication --- distributed ledger --- identity management --- RAFT --- HL7 --- electronic health record --- Hyperledger Composer --- cybersecurity --- password security --- browser security --- social media --- ANOVA --- SPSS --- internet of things --- cloud computing --- computational models --- metaheuristics --- phishing detection --- website phishing --- fintech --- financial technology --- blockchain --- deep learning --- regtech --- environment --- social sciences --- machine learning --- learning analytics --- student field forecasting --- imbalanced datasets --- explainable machine learning --- intelligent tutoring system --- adversarial machine learning --- transfer learning --- cognitive bias --- stock market --- behavioural finance --- investor’s profile --- Teheran Stock Exchange --- unsupervised learning --- clustering --- big data frameworks --- fault tolerance --- stream processing systems --- distributed frameworks --- Spark --- Hadoop --- Storm --- Samza --- Flink --- comparative analysis --- a survey --- data science --- educational data mining --- supervised learning --- secondary education --- academic performance --- text-to-SQL --- natural language processing --- database --- machine translation --- medical image segmentation --- convolutional neural networks --- SE block --- U-net --- DeepLabV3plus --- cyber-security --- medical services --- cyber-attacks --- data communication --- distributed ledger --- identity management --- RAFT --- HL7 --- electronic health record --- Hyperledger Composer --- cybersecurity --- password security --- browser security --- social media --- ANOVA --- SPSS --- internet of things --- cloud computing --- computational models --- metaheuristics --- phishing detection --- website phishing
Listing 1 - 10 of 11 | << page >> |
Sort by
|