Web App Development Made Simple with Streamlit

Web App Development Made Simple with Streamlit

Author: Rosario Moscato

Publisher: Packt Publishing Ltd

Published: 2024-02-09

Total Pages: 350

ISBN-13: 1835085938

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Unlock the full potential of Streamlit, mastering web app development from setup to deployment with practical guidance, advanced techniques, and real-world examples Key Features Identify and overcome web development challenges, crafting dedicated application skeletons using Streamlit Understand how Streamlit's widgets and components work to implement any kind of web app Manage web application development and deployment with ease using the Streamlit Cloud service Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis book is a comprehensive guide to the Streamlit open-source Python library and simplifying the process of creating web applications. Through hands-on guidance and realistic examples, you’ll progress from crafting simple to sophisticated web applications from scratch. This book covers everything from understanding Streamlit's central principles, modules, basic features, and widgets to advanced skills such as dealing with databases, hashes, sessions, and multipages. Starting with fundamental concepts like operation systems virtualization, IDEs, development environments, widgets, scripting, and the anatomy of web apps, the initial chapters set the groundwork. You’ll then apply this knowledge to develop some real web apps, gradually advancing to more complex apps, incorporating features like natural language processing (NLP), computer vision, dashboards with interactive charts, file uploading, and much more. The book concludes by delving into the implementation of advanced skills and deployment techniques. By the end of this book, you’ll have transformed into a proficient developer, equipped with advanced skills for handling databases, implementing secure login processes, managing session states, creating multipage applications, and seamlessly deploying them on the cloud.What you will learn Develop interactive web apps with Streamlit and deploy them seamlessly on the cloud Acquire in-depth theoretical and practical expertise in using Streamlit for app development Use themes and customization for visually appealing web apps tailored to specific needs Implement advanced features including secure login, signup processes, file uploaders, and database connections Build a catalog of scripts and routines to efficiently implement new web apps Attain autonomy in adopting new Streamlit features rapidly and effectively Who this book is for This book is for Python programmers, web developers, computer science students, and IT enthusiasts with a foundation in Python (or any programming language) who have a passion for creating visually appealing applications. If you already know how to write programs, this book will help you evolve into an adept web application developer skilled at converting command-line tools into impressive, cloud-hosted applications.


Book Synopsis Web App Development Made Simple with Streamlit by : Rosario Moscato

Download or read book Web App Development Made Simple with Streamlit written by Rosario Moscato and published by Packt Publishing Ltd. This book was released on 2024-02-09 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the full potential of Streamlit, mastering web app development from setup to deployment with practical guidance, advanced techniques, and real-world examples Key Features Identify and overcome web development challenges, crafting dedicated application skeletons using Streamlit Understand how Streamlit's widgets and components work to implement any kind of web app Manage web application development and deployment with ease using the Streamlit Cloud service Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis book is a comprehensive guide to the Streamlit open-source Python library and simplifying the process of creating web applications. Through hands-on guidance and realistic examples, you’ll progress from crafting simple to sophisticated web applications from scratch. This book covers everything from understanding Streamlit's central principles, modules, basic features, and widgets to advanced skills such as dealing with databases, hashes, sessions, and multipages. Starting with fundamental concepts like operation systems virtualization, IDEs, development environments, widgets, scripting, and the anatomy of web apps, the initial chapters set the groundwork. You’ll then apply this knowledge to develop some real web apps, gradually advancing to more complex apps, incorporating features like natural language processing (NLP), computer vision, dashboards with interactive charts, file uploading, and much more. The book concludes by delving into the implementation of advanced skills and deployment techniques. By the end of this book, you’ll have transformed into a proficient developer, equipped with advanced skills for handling databases, implementing secure login processes, managing session states, creating multipage applications, and seamlessly deploying them on the cloud.What you will learn Develop interactive web apps with Streamlit and deploy them seamlessly on the cloud Acquire in-depth theoretical and practical expertise in using Streamlit for app development Use themes and customization for visually appealing web apps tailored to specific needs Implement advanced features including secure login, signup processes, file uploaders, and database connections Build a catalog of scripts and routines to efficiently implement new web apps Attain autonomy in adopting new Streamlit features rapidly and effectively Who this book is for This book is for Python programmers, web developers, computer science students, and IT enthusiasts with a foundation in Python (or any programming language) who have a passion for creating visually appealing applications. If you already know how to write programs, this book will help you evolve into an adept web application developer skilled at converting command-line tools into impressive, cloud-hosted applications.

Getting Started with Streamlit for Data Science

Getting Started with Streamlit for Data Science

Author: Tyler Richards

Publisher: Packt Publishing Ltd

Published: 2021-08-20

Total Pages: 282

ISBN-13: 1800563205

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Create, deploy, and test your Python applications, analyses, and models with ease using Streamlit Key Features Learn how to showcase machine learning models in a Streamlit application effectively and efficiently Become an expert Streamlit creator by getting hands-on with complex application creation Discover how Streamlit enables you to create and deploy apps effortlessly Book DescriptionStreamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.What you will learn Set up your first development environment and create a basic Streamlit app from scratch Explore methods for uploading, downloading, and manipulating data in Streamlit apps Create dynamic visualizations in Streamlit using built-in and imported Python libraries Discover strategies for creating and deploying machine learning models in Streamlit Use Streamlit sharing for one-click deployment Beautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebar Implement best practices for prototyping your data science work with Streamlit Who this book is for This book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you’re a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered.


Book Synopsis Getting Started with Streamlit for Data Science by : Tyler Richards

Download or read book Getting Started with Streamlit for Data Science written by Tyler Richards and published by Packt Publishing Ltd. This book was released on 2021-08-20 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create, deploy, and test your Python applications, analyses, and models with ease using Streamlit Key Features Learn how to showcase machine learning models in a Streamlit application effectively and efficiently Become an expert Streamlit creator by getting hands-on with complex application creation Discover how Streamlit enables you to create and deploy apps effortlessly Book DescriptionStreamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.What you will learn Set up your first development environment and create a basic Streamlit app from scratch Explore methods for uploading, downloading, and manipulating data in Streamlit apps Create dynamic visualizations in Streamlit using built-in and imported Python libraries Discover strategies for creating and deploying machine learning models in Streamlit Use Streamlit sharing for one-click deployment Beautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebar Implement best practices for prototyping your data science work with Streamlit Who this book is for This book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you’re a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered.

Web Application Development with Streamlit

Web Application Development with Streamlit

Author: Mohammad Khorasani

Publisher: Apress

Published: 2022-08-27

Total Pages: 0

ISBN-13: 9781484281109

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Transition from a back-end developer to a full-stack developer with knowledge of all the dimensions of web application development, namely, front-end, back-end and server-side software. This book provides a comprehensive overview of Streamlit, allowing developers and programmers of all backgrounds to get up to speed in as little time as possible. Streamlit is a pure Python web framework that will bridge the skills gap and shorten development time from weeks to hours. This book walks you through the complete cycle of web application development, from an introductory to advanced level with accompanying source code and resources. You will be exposed to developing basic, intermediate, and sophisticated user interfaces and subsequently you will be acquainted with data visualization, database systems, application security, and cloud deployment in Streamlit. In a market with a surplus demand for full stack developers, this skill set could not possibly come at a better time. In one sentence, Streamlit is a means for the empowerment of developers everywhere and all stand to gain from it. What You’ll Learn Mutate big data in real-time Visualize big data interactively Implement web application security and privacy protocols Deploy Streamlit web applications to the cloud using Streamlit, Linux and Windows servers Who is this Book for? Developers with solid programming experience wanting to learn Streamlit; Back-end developers looking to upskill and transition to become a full-stack developers; Those who wish to learn and become more acquainted with data visualization, database systems, security and cloud deployment with Steamlit


Book Synopsis Web Application Development with Streamlit by : Mohammad Khorasani

Download or read book Web Application Development with Streamlit written by Mohammad Khorasani and published by Apress. This book was released on 2022-08-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transition from a back-end developer to a full-stack developer with knowledge of all the dimensions of web application development, namely, front-end, back-end and server-side software. This book provides a comprehensive overview of Streamlit, allowing developers and programmers of all backgrounds to get up to speed in as little time as possible. Streamlit is a pure Python web framework that will bridge the skills gap and shorten development time from weeks to hours. This book walks you through the complete cycle of web application development, from an introductory to advanced level with accompanying source code and resources. You will be exposed to developing basic, intermediate, and sophisticated user interfaces and subsequently you will be acquainted with data visualization, database systems, application security, and cloud deployment in Streamlit. In a market with a surplus demand for full stack developers, this skill set could not possibly come at a better time. In one sentence, Streamlit is a means for the empowerment of developers everywhere and all stand to gain from it. What You’ll Learn Mutate big data in real-time Visualize big data interactively Implement web application security and privacy protocols Deploy Streamlit web applications to the cloud using Streamlit, Linux and Windows servers Who is this Book for? Developers with solid programming experience wanting to learn Streamlit; Back-end developers looking to upskill and transition to become a full-stack developers; Those who wish to learn and become more acquainted with data visualization, database systems, security and cloud deployment with Steamlit

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development

Author: Kunal Roy

Publisher: Elsevier

Published: 2023-05-23

Total Pages: 768

ISBN-13: 0443186391

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Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates. Presents chemometrics, cheminformatics and machine learning methods under a single reference Showcases the different structure-based, ligand-based and machine learning tools currently used in drug design Highlights special topics of computational drug design and available tools and databases


Book Synopsis Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development by : Kunal Roy

Download or read book Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development written by Kunal Roy and published by Elsevier. This book was released on 2023-05-23 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates. Presents chemometrics, cheminformatics and machine learning methods under a single reference Showcases the different structure-based, ligand-based and machine learning tools currently used in drug design Highlights special topics of computational drug design and available tools and databases

Beginner's Guide to Streamlit with Python

Beginner's Guide to Streamlit with Python

Author: Sujay Raghavendra

Publisher:

Published: 2023

Total Pages: 0

ISBN-13: 9781484294147

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This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you'll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models. Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You'll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you'll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit. After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own. You will: Start developing web applications using Streamlit Understand Streamlit's components Utilize media elements in Streamlit Visualize data using various interactive and dynamic Python libraries Implement models in Streamlit web applications.


Book Synopsis Beginner's Guide to Streamlit with Python by : Sujay Raghavendra

Download or read book Beginner's Guide to Streamlit with Python written by Sujay Raghavendra and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you'll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models. Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You'll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you'll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit. After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own. You will: Start developing web applications using Streamlit Understand Streamlit's components Utilize media elements in Streamlit Visualize data using various interactive and dynamic Python libraries Implement models in Streamlit web applications.

Streamlit for Data Science

Streamlit for Data Science

Author: Tyler Richards

Publisher: Packt Publishing Ltd

Published: 2023-09-29

Total Pages: 301

ISBN-13: 1803232951

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An easy-to-follow and comprehensive guide to creating data apps with Streamlit, including how-to guides for working with cloud data warehouses like Snowflake, using pretrained Hugging Face and OpenAI models, and creating apps for job interviews. Key Features Create machine learning apps with random forest, Hugging Face, and GPT-3.5 turbo models Gain an insight into how experts harness Streamlit with in-depth interviews with Streamlit power users Discover the full range of Streamlit’s capabilities via hands-on exercises to effortlessly create and deploy well-designed apps Book DescriptionIf you work with data in Python and are looking to create data apps that showcase ML models and make beautiful interactive visualizations, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy data apps quickly, all within Python. This helps you create prototypes in hours instead of days! Written by a prolific Streamlit user and senior data scientist at Snowflake, this fully updated second edition builds on the practical nature of the previous edition with exciting updates, including connecting Streamlit to data warehouses like Snowflake, integrating Hugging Face and OpenAI models into your apps, and connecting and building apps on top of Streamlit databases. Plus, there is a totally updated code repository on GitHub to help you practice your newfound skills. You'll start your journey with the fundamentals of Streamlit and gradually build on this foundation by working with machine learning models and producing high-quality interactive apps. The practical examples of both personal data projects and work-related data-focused web applications will help you get to grips with more challenging topics such as Streamlit Components, beautifying your apps, and quick deployment. By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly.What you will learn Set up your first development environment and create a basic Streamlit app from scratch Create dynamic visualizations using built-in and imported Python libraries Discover strategies for creating and deploying machine learning models in Streamlit Deploy Streamlit apps with Streamlit Community Cloud, Hugging Face Spaces, and Heroku Integrate Streamlit with Hugging Face, OpenAI, and Snowflake Beautify Streamlit apps using themes and components Implement best practices for prototyping your data science work with Streamlit Who this book is forThis book is for data scientists and machine learning enthusiasts who want to get started with creating data apps in Streamlit. It is terrific for junior data scientists looking to gain some valuable new skills in a specific and actionable fashion and is also a great resource for senior data scientists looking for a comprehensive overview of the library and how people use it. Prior knowledge of Python programming is a must, and you’ll get the most out of this book if you’ve used Python libraries like Pandas and NumPy in the past.


Book Synopsis Streamlit for Data Science by : Tyler Richards

Download or read book Streamlit for Data Science written by Tyler Richards and published by Packt Publishing Ltd. This book was released on 2023-09-29 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: An easy-to-follow and comprehensive guide to creating data apps with Streamlit, including how-to guides for working with cloud data warehouses like Snowflake, using pretrained Hugging Face and OpenAI models, and creating apps for job interviews. Key Features Create machine learning apps with random forest, Hugging Face, and GPT-3.5 turbo models Gain an insight into how experts harness Streamlit with in-depth interviews with Streamlit power users Discover the full range of Streamlit’s capabilities via hands-on exercises to effortlessly create and deploy well-designed apps Book DescriptionIf you work with data in Python and are looking to create data apps that showcase ML models and make beautiful interactive visualizations, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy data apps quickly, all within Python. This helps you create prototypes in hours instead of days! Written by a prolific Streamlit user and senior data scientist at Snowflake, this fully updated second edition builds on the practical nature of the previous edition with exciting updates, including connecting Streamlit to data warehouses like Snowflake, integrating Hugging Face and OpenAI models into your apps, and connecting and building apps on top of Streamlit databases. Plus, there is a totally updated code repository on GitHub to help you practice your newfound skills. You'll start your journey with the fundamentals of Streamlit and gradually build on this foundation by working with machine learning models and producing high-quality interactive apps. The practical examples of both personal data projects and work-related data-focused web applications will help you get to grips with more challenging topics such as Streamlit Components, beautifying your apps, and quick deployment. By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly.What you will learn Set up your first development environment and create a basic Streamlit app from scratch Create dynamic visualizations using built-in and imported Python libraries Discover strategies for creating and deploying machine learning models in Streamlit Deploy Streamlit apps with Streamlit Community Cloud, Hugging Face Spaces, and Heroku Integrate Streamlit with Hugging Face, OpenAI, and Snowflake Beautify Streamlit apps using themes and components Implement best practices for prototyping your data science work with Streamlit Who this book is forThis book is for data scientists and machine learning enthusiasts who want to get started with creating data apps in Streamlit. It is terrific for junior data scientists looking to gain some valuable new skills in a specific and actionable fashion and is also a great resource for senior data scientists looking for a comprehensive overview of the library and how people use it. Prior knowledge of Python programming is a must, and you’ll get the most out of this book if you’ve used Python libraries like Pandas and NumPy in the past.

Guide to Web Application and Platform Architectures

Guide to Web Application and Platform Architectures

Author: Stefan Jablonski

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 245

ISBN-13: 3662076314

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New concepts and technologies are being introduced continuously for application development in the World-Wide Web. Selecting the right implementation strategies and tools when building a Web application has become a tedious task, requiring in-depth knowledge and significant experience from both software developers and software managers. The mission of this book is to guide the reader through the opaque jungle of Web technologies. Based on their long industrial and academic experience, Stefan Jablonski and his coauthors provide a framework architecture for Web applications which helps choose the best strategy for a given project. The authors classify common technologies and standards like .NET, CORBA, J2EE, DCOM, WSDL and many more with respect to platform, architectural layer, and application package, and guide the reader through a three-phase development process consisting of preparation, design, and technology selection steps. The whole approach is exemplified using a real-world case: the architectural design of an order-entry management system.


Book Synopsis Guide to Web Application and Platform Architectures by : Stefan Jablonski

Download or read book Guide to Web Application and Platform Architectures written by Stefan Jablonski and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: New concepts and technologies are being introduced continuously for application development in the World-Wide Web. Selecting the right implementation strategies and tools when building a Web application has become a tedious task, requiring in-depth knowledge and significant experience from both software developers and software managers. The mission of this book is to guide the reader through the opaque jungle of Web technologies. Based on their long industrial and academic experience, Stefan Jablonski and his coauthors provide a framework architecture for Web applications which helps choose the best strategy for a given project. The authors classify common technologies and standards like .NET, CORBA, J2EE, DCOM, WSDL and many more with respect to platform, architectural layer, and application package, and guide the reader through a three-phase development process consisting of preparation, design, and technology selection steps. The whole approach is exemplified using a real-world case: the architectural design of an order-entry management system.

Web Service APIs and Libraries

Web Service APIs and Libraries

Author: Jason Paul Michel

Publisher: American Library Association

Published: 2013

Total Pages: 154

ISBN-13: 083891182X

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This book shows how to enhance an institution's presence on the Web with tools that integrate a variety of handy, popular programs. Application Programming Interfaces (APIs) are software tools that help different programs work together, and Michel shows readers how to integrate them into existing library websites as well as use them to launch new kinds of services. Offering step-by-step guidance, this book Uses real-world examples to show how APIs can be used to promote library materials and events, visualize data, educate patrons, and mobilize library services Demonstrates how to create and manage widgets for photo galleries, instant reporting on computer/printer availability, featured book titles and book reviews from library users, tracking usage data, and many other library functions Includes instructions for working with popular tools such as Flickr, YouTube, Vimeo, Twitter, Google Charts, OCLC, WordPress, Goodreads, LibraryThing, and the Hathi Trust Provides plentiful screenshots, snippets of HTML code, and easy-to-follow samples to ensure that even novices will feel comfortable integrating APIs into their marketing plans Focusing on widely adopted tools that all have immediate, useful applications, this practical book will help extend any library’s reach.


Book Synopsis Web Service APIs and Libraries by : Jason Paul Michel

Download or read book Web Service APIs and Libraries written by Jason Paul Michel and published by American Library Association. This book was released on 2013 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how to enhance an institution's presence on the Web with tools that integrate a variety of handy, popular programs. Application Programming Interfaces (APIs) are software tools that help different programs work together, and Michel shows readers how to integrate them into existing library websites as well as use them to launch new kinds of services. Offering step-by-step guidance, this book Uses real-world examples to show how APIs can be used to promote library materials and events, visualize data, educate patrons, and mobilize library services Demonstrates how to create and manage widgets for photo galleries, instant reporting on computer/printer availability, featured book titles and book reviews from library users, tracking usage data, and many other library functions Includes instructions for working with popular tools such as Flickr, YouTube, Vimeo, Twitter, Google Charts, OCLC, WordPress, Goodreads, LibraryThing, and the Hathi Trust Provides plentiful screenshots, snippets of HTML code, and easy-to-follow samples to ensure that even novices will feel comfortable integrating APIs into their marketing plans Focusing on widely adopted tools that all have immediate, useful applications, this practical book will help extend any library’s reach.

Deep Learning for Genomics

Deep Learning for Genomics

Author: Upendra Kumar Devisetty

Publisher: Packt Publishing Ltd

Published: 2022-11-11

Total Pages: 270

ISBN-13: 1804613010

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Learn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industries Key FeaturesApply deep learning algorithms to solve real-world problems in the field of genomicsExtract biological insights from deep learning models built from genomic datasetsTrain, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomicsBook Description Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you'll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics. What you will learnDiscover the machine learning applications for genomicsExplore deep learning concepts and methodologies for genomics applicationsUnderstand supervised deep learning algorithms for genomics applicationsGet to grips with unsupervised deep learning with autoencodersImprove deep learning models using generative modelsOperationalize deep learning models from genomics datasetsVisualize and interpret deep learning modelsUnderstand deep learning challenges, pitfalls, and best practicesWho this book is for This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.


Book Synopsis Deep Learning for Genomics by : Upendra Kumar Devisetty

Download or read book Deep Learning for Genomics written by Upendra Kumar Devisetty and published by Packt Publishing Ltd. This book was released on 2022-11-11 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industries Key FeaturesApply deep learning algorithms to solve real-world problems in the field of genomicsExtract biological insights from deep learning models built from genomic datasetsTrain, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomicsBook Description Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you'll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics. What you will learnDiscover the machine learning applications for genomicsExplore deep learning concepts and methodologies for genomics applicationsUnderstand supervised deep learning algorithms for genomics applicationsGet to grips with unsupervised deep learning with autoencodersImprove deep learning models using generative modelsOperationalize deep learning models from genomics datasetsVisualize and interpret deep learning modelsUnderstand deep learning challenges, pitfalls, and best practicesWho this book is for This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.

Python Tools for Scientists

Python Tools for Scientists

Author: Lee Vaughan

Publisher: No Starch Press

Published: 2023-01-17

Total Pages: 744

ISBN-13: 1718502672

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An introduction to the Python programming language and its most popular tools for scientists, engineers, students, and anyone who wants to use Python for research, simulations, and collaboration. Python Tools for Scientists will introduce you to Python tools you can use in your scientific research, including Anaconda, Spyder, Jupyter Notebooks, JupyterLab, and numerous Python libraries. You’ll learn to use Python for tasks such as creating visualizations, representing geospatial information, simulating natural events, and manipulating numerical data. Once you’ve built an optimal programming environment with Anaconda, you’ll learn how to organize your projects and use interpreters, text editors, notebooks, and development environments to work with your code. Following the book’s fast-paced Python primer, you’ll tour a range of scientific tools and libraries like scikit-learn and seaborn that you can use to manipulate and visualize your data, or analyze it with machine learning algorithms. You’ll also learn how to: Create isolated projects in virtual environments, build interactive notebooks, test code in the Qt console, and use Spyder’s interactive development features Use Python’s built-in data types, write custom functions and classes, and document your code Represent data with the essential NumPy, Matplotlib, and pandas libraries Use Python plotting libraries like Plotly, HoloViews, and Datashader to handle large datasets and create 3D visualizations Regardless of your scientific field, Python Tools for Scientists will show you how to choose the best tools to meet your research and computational analysis needs.


Book Synopsis Python Tools for Scientists by : Lee Vaughan

Download or read book Python Tools for Scientists written by Lee Vaughan and published by No Starch Press. This book was released on 2023-01-17 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the Python programming language and its most popular tools for scientists, engineers, students, and anyone who wants to use Python for research, simulations, and collaboration. Python Tools for Scientists will introduce you to Python tools you can use in your scientific research, including Anaconda, Spyder, Jupyter Notebooks, JupyterLab, and numerous Python libraries. You’ll learn to use Python for tasks such as creating visualizations, representing geospatial information, simulating natural events, and manipulating numerical data. Once you’ve built an optimal programming environment with Anaconda, you’ll learn how to organize your projects and use interpreters, text editors, notebooks, and development environments to work with your code. Following the book’s fast-paced Python primer, you’ll tour a range of scientific tools and libraries like scikit-learn and seaborn that you can use to manipulate and visualize your data, or analyze it with machine learning algorithms. You’ll also learn how to: Create isolated projects in virtual environments, build interactive notebooks, test code in the Qt console, and use Spyder’s interactive development features Use Python’s built-in data types, write custom functions and classes, and document your code Represent data with the essential NumPy, Matplotlib, and pandas libraries Use Python plotting libraries like Plotly, HoloViews, and Datashader to handle large datasets and create 3D visualizations Regardless of your scientific field, Python Tools for Scientists will show you how to choose the best tools to meet your research and computational analysis needs.