LEADER 01115nam 2200253la 450 001 9910482828303321 005 20221107235426.0 035 $a(UK-CbPIL)2090323450 035 $a(CKB)5500000000094891 035 $a(EXLCZ)995500000000094891 100 $a20210618d1520 uy | 101 0 $alat 135 $aurcn||||a|bb| 200 10$aHortulus Synonymorum ad usum Danorum concinnatus : cui libellus de vocum proprietatibus subiunctus est tam solutam orationem quam ligatam scribere volentibus oppidoque utilis, Henr. Faber [Henrik Smith]$b[electronic resource] 210 $aCopenhagen $c[s.n.]$d1520 215 $aOnline resource (68 bl.) 300 $aReproduction of original in Det Kongelige Bibliotek / The Royal Library (Copenhagen). 700 $aSmith$b Henrik$f1563.$0862103 801 0$bUk-CbPIL 801 1$bUk-CbPIL 906 $aBOOK 912 $a9910482828303321 996 $aHortulus Synonymorum ad usum Danorum concinnatus : cui libellus de vocum proprietatibus subiunctus est tam solutam orationem quam ligatam scribere volentibus oppidoque utilis, Henr. Faber$91959019 997 $aUNINA LEADER 05495nam 22006135 450 001 9910300362903321 005 20200706124655.0 010 $a9781484238738 010 $a1484238737 024 7 $a10.1007/978-1-4842-3873-8 035 $a(CKB)4100000006519928 035 $a(MiAaPQ)EBC5516214 035 $a(DE-He213)978-1-4842-3873-8 035 $a(CaSebORM)9781484238738 035 $a(PPN)230542743 035 $a(OCoLC)1056626553 035 $a(OCoLC)on1056626553 035 $a(EXLCZ)994100000006519928 100 $a20180912d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMonetizing Machine Learning $eQuickly Turn Python ML Ideas into Web Applications on the Serverless Cloud /$fby Manuel Amunategui, Mehdi Roopaei 205 $a1st ed. 2018. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2018. 215 $a1 online resource (510 pages) 311 08$a9781484238721 311 08$a1484238729 320 $aIncludes bibliographical references. 327 $aChapter 1 Introduction to Serverless Technologies -- Chapter 2 Client-Side Intelligence using Regression Coefficients on Azure -- Chapter 3 Real-Time Intelligence with Logistic Regression on GCP -- Chapter 4 Pre-Trained Intelligence with Gradient Boosting Machine on AWS -- Chapter 5 Case Study Part 1: Supporting Both Web and Mobile Browsers -- Chapter 6 Displaying Predictions with Google Maps on Azure -- Chapter 7 Forecasting with Naive Bayes and OpenWeather on AWS -- Chapter 8 Interactive Drawing Canvas and Digit Predictions using TensorFlow on GCP -- Chapter 9 Case Study Part 2: Displaying Dynamic Charts -- Chapter 10 Recommending with Singular Value Decomposition on GCP -- Chapter 11 Simplifying Complex Concepts with NLP and Visualization on Azure -- Chapter 12 Case Study Part 3: Enriching Content with Fundamental Financial Information -- Chapter 13 Google Analytics -- Chapter 14 A/B Testing on PythonAnywhere and MySQL -- Chapter 15 From Visitor To Subscriber -- Chapter 16 Case Study Part 4: Building a Subscription Paywall with Memberful -- Chapter 17 Conclusion.-. 330 $aTake your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book?Amazon, Microsoft, Google, and PythonAnywhere. You will work through a series of common Python data science problems in an increasing order of complexity. The practical projects presented in this book are simple, clear, and can be used as templates to jump-start many other types of projects. You will learn to create a web application around numerical or categorical predictions, understand the analysis of text, create powerful and interactive presentations, serve restricted access to data, and leverage web plugins to accept credit card payments and donations. You will get your projects into the hands of the world in no time. Each chapter follows three steps: modeling the right way, designing and developing a local web application, and deploying onto a popular and reliable serverless cloud provider. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book. What You?ll Learn: Extend your machine learning models using simple techniques to create compelling and interactive web dashboards Leverage the Flask web framework for rapid prototyping of your Python models and ideas Create dynamic content powered by regression coefficients, logistic regressions, gradient boosting machines, Bayesian classifications, and more Harness the power of TensorFlow by exporting saved models into web applications Create rich web dashboards to handle complex real-time user input with JavaScript and Ajax to yield interactive and tailored content Create dashboards with paywalls to offer subscription-based access Access API data such as Google Maps, OpenWeather, etc. Apply different approaches to make sense of text data and return customized intelligence Build an intuitive and useful recommendation site to add value to users and entice them to keep coming back Utilize the freemium offerings of Google Analytics and analyze the results Take your ideas all the way to your customer's plate using the top serverless cloud providers. 606 $aArtificial intelligence 606 $aComputer networks 606 $aBig data 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 615 0$aArtificial intelligence. 615 0$aComputer networks. 615 0$aBig data. 615 14$aArtificial Intelligence. 615 24$aComputer Communication Networks. 615 24$aBig Data. 676 $a006.31 700 $aAmunategui$b Manuel$4aut$4http://id.loc.gov/vocabulary/relators/aut$01057792 702 $aRoopaei$b Mehdi$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910300362903321 996 $aMonetizing Machine Learning$92494530 997 $aUNINA LEADER 00604nam 2200229zu 450 001 9911049044803321 005 20260109151625.0 010 $a3-96147-571-7 035 $a(CKB)44900855100041 035 $a(EXLCZ)9944900855100041 100 $a20260109|2022uuuu || | 101 0 $aeng 135 $aur||||||||||| 200 10$aInvasive Computing 210 $cFAU University Press$d2022 311 08$a3-96147-570-9 700 $aAnantharajaiah$b Nidhi$01888045 701 $aHenkel$b Jörg$0763590 906 $aBOOK 912 $a9911049044803321 996 $aInvasive Computing$94526344 997 $aUNINA