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SN Video Coding and Web Development, Beginning Lighting and Rendering with 3ds Max and V-Ray
presented by Jamie Cardoso, fl. 2007; produced by Springer Nature, in SN Video Coding and Web Development (New York, NY: Springer Nature, 2219), 50 mins
Explore V-Ray lighting and rendering with 3ds Max to add professional effects to all of your projects. In this video, you will work with V-Ray parameters, tolls, modifiers, and lights through practical examples. Along the way, you will see many useful tips and tricks, and adopt best practices with workflows, techn...
Sample
presented by Jamie Cardoso, fl. 2007; produced by Springer Nature, in SN Video Coding and Web Development (New York, NY: Springer Nature, 2219), 50 mins
Description
Explore V-Ray lighting and rendering with 3ds Max to add professional effects to all of your projects. In this video, you will work with V-Ray parameters, tolls, modifiers, and lights through practical examples. Along the way, you will see many useful tips and tricks, and adopt best practices with workflows, techniques, and methodologies to gain a solid foundation to consistently create amazing lighting.
What You Will Learn:
Create consistent an...
Explore V-Ray lighting and rendering with 3ds Max to add professional effects to all of your projects. In this video, you will work with V-Ray parameters, tolls, modifiers, and lights through practical examples. Along the way, you will see many useful tips and tricks, and adopt best practices with workflows, techniques, and methodologies to gain a solid foundation to consistently create amazing lighting.
What You Will Learn:
Create consistent and high-quality renders
Understand how to troubleshoot rendering errors
Build a solid understanding of render elements
Who This Video Is For:
3D visualizers, designers, architects, and interior designers looking to enhance their lighting and rendering skills with 3ds Max and V-Ray.
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Field of Study
Education
Content Type
Instructional material
Contributor
Springer Nature
Author / Creator
Jamie Cardoso, fl. 2007
Date Published / Released
2219
Publisher
Springer Nature
Series
SN Video Coding and Web Development
Copyright Message
Copyright © 2019 Springer Nature
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NOVA, Season 48, Episode 1, Secrets in Our DNA
directed by Edward Gray, fl. 1979; produced by Michael Schwarz, fl. 2002 and Kiki Kapany, fl. 2002, Kikim Media and WGBH Educational Foundation, in NOVA, Season 48, Episode 1 (Arlington, VA: Public Broadcasting Service, 2021), 53 mins
Millions have sent their DNA to be analyzed by private companies, which are revealing customers' ancestry and family connections, and reporting health risks. But what are the pitfalls and unintended consequences of sharing genetic data?
Sample
directed by Edward Gray, fl. 1979; produced by Michael Schwarz, fl. 2002 and Kiki Kapany, fl. 2002, Kikim Media and WGBH Educational Foundation, in NOVA, Season 48, Episode 1 (Arlington, VA: Public Broadcasting Service, 2021), 53 mins
Description
Millions have sent their DNA to be analyzed by private companies, which are revealing customers' ancestry and family connections, and reporting health risks. But what are the pitfalls and unintended consequences of sharing genetic data?
Field of Study
Science
Content Type
Documentary
Contributor
Michael Schwarz, fl. 2002, Kiki Kapany, fl. 2002, Kikim Media, WGBH Educational Foundation, Talithia Williams
Author / Creator
Edward Gray, fl. 1979
Date Published / Released
2021
Publisher
Public Broadcasting Service
Series
NOVA
Speaker / Narrator
Talithia Williams
Topic / Theme
Genealogy, Criminal investigations, Privacy rights, Genetic counseling, Genetics
Copyright Message
Copyright © 2021 WGBH Educational Foundation
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NOVA, Season 48, Episode 2, Looking for Life on Mars
directed by Terri Randall, fl. 1997-2019; produced by Jedd Ehrmann, fl. 1998 and Terri Randall, fl. 1997-2019, Terri Randall Film and Video Productions, in NOVA, Season 48, Episode 2 (Arlington, VA: Public Broadcasting Service, 2021), 53 mins
NASA launches its most ambitious hunt for traces of life on Mars, landing a rover in a rocky, ancient river delta. The rover will stow samples for possible return to Earth and test technology that may pave the way for human travel to Mars.
Sample
directed by Terri Randall, fl. 1997-2019; produced by Jedd Ehrmann, fl. 1998 and Terri Randall, fl. 1997-2019, Terri Randall Film and Video Productions, in NOVA, Season 48, Episode 2 (Arlington, VA: Public Broadcasting Service, 2021), 53 mins
Description
NASA launches its most ambitious hunt for traces of life on Mars, landing a rover in a rocky, ancient river delta. The rover will stow samples for possible return to Earth and test technology that may pave the way for human travel to Mars.
Field of Study
Science
Content Type
Documentary
Contributor
Jedd Ehrmann, fl. 1998, Terri Randall, fl. 1997-2019, Terri Randall Film and Video Productions, Craig Sechler, fl. 1990
Author / Creator
Terri Randall, fl. 1997-2019
Date Published / Released
2021
Publisher
Public Broadcasting Service
Series
NOVA
Speaker / Narrator
Craig Sechler, fl. 1990
Topic / Theme
Space exploration, Planets
Copyright Message
Copyright © 2021 WGBH Educational Foundation
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SN Video Coding and Web Development, Asynchronous Programming in Python
presented by Coen de Groot; produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 26 mins
In this course, we will look at using asynchronous programming in Python: the options, pitfalls, and best practices. We start with multi-threading, which is particularly useful when there is a lot of waiting, e.g. for HTTP requests or disk access. With multi-threading, you can start many requests in quick successi...
Sample
presented by Coen de Groot; produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 26 mins
Description
In this course, we will look at using asynchronous programming in Python: the options, pitfalls, and best practices. We start with multi-threading, which is particularly useful when there is a lot of waiting, e.g. for HTTP requests or disk access. With multi-threading, you can start many requests in quick succession and then wait for all of them to complete at once. Next, the course will show you how to write your code in a thread-safe manner, an...
In this course, we will look at using asynchronous programming in Python: the options, pitfalls, and best practices. We start with multi-threading, which is particularly useful when there is a lot of waiting, e.g. for HTTP requests or disk access. With multi-threading, you can start many requests in quick succession and then wait for all of them to complete at once. Next, the course will show you how to write your code in a thread-safe manner, and how to use it risk-free. Further, it covers Python’s global interpreter lock, which prevents a lot of serious problems in Python but also stops you from running threads in parallel.
Going forward we discover how you can use Python’s multiprocessing library to run functions in parallel. Threads and processes often need to share or exchange data. In asynchronous code just passing Python objects is usually not the safest way to do this. This course looks at the main ways to do this correctly in Python, such as queues and events. Finally, the course moves on to the concurrent.future library which contains higher-level abstractions, including thread and processing pools and an asynchronous map function.The course finishes with advice on how to write robust asynchronous code, and how to test and debug it.What You Will Learn: Take advantage of multi threading (concurrency) using the threading module; Implement multi processing (parallelism) using the multiprocessing module; Understand inter-process communication and data sharing using locks, queues, semaphores, barriers, events, and timers; Test and debug asynchronous Python code; Understand thread and process pools using concurrent.futures. Who This Video Is For: Intermediate to experienced Python programmers who want to speed up existing Python code by using multi processing and multi threading.
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Field of Study
Science
Content Type
Instructional material
Contributor
Springer Nature
Author / Creator
Coen de Groot
Date Published / Released
2021
Publisher
Springer Nature
Series
SN Video Coding and Web Development
Topic / Theme
Software engineering, Computer software
Copyright Message
Copyright © 2021 Springer Nature B.V.
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SN Video Coding and Web Development, Asynchronous Programming with Python
produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 26 mins
In this course, we will look at using asynchronous programming in Python: the options, pitfalls, and best practices. We start with multi-threading, which is particularly useful when there is a lot of waiting, e.g. for HTTP requests or disk access. With multi-threading, you can start many requests in quick successi...
Sample
produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 26 mins
Description
In this course, we will look at using asynchronous programming in Python: the options, pitfalls, and best practices. We start with multi-threading, which is particularly useful when there is a lot of waiting, e.g. for HTTP requests or disk access. With multi-threading, you can start many requests in quick succession and then wait for all of them to complete at once. Next, the course will show you how to write your code in a thread-safe manner, an...
In this course, we will look at using asynchronous programming in Python: the options, pitfalls, and best practices. We start with multi-threading, which is particularly useful when there is a lot of waiting, e.g. for HTTP requests or disk access. With multi-threading, you can start many requests in quick succession and then wait for all of them to complete at once. Next, the course will show you how to write your code in a thread-safe manner, and how to use it risk-free. Further, it covers Python’s global interpreter lock, which prevents a lot of serious problems in Python but also stops you from running threads in parallel.
Going forward we discover how you can use Python’s multiprocessing library to run functions in parallel. Threads and processes often need to share or exchange data. In asynchronous code just passing Python objects is usually not the safest way to do this. This course looks at the main ways to do this correctly in Python, such as queues and events. Finally, the course moves on to the concurrent.future library which contains higher-level abstractions, including thread and processing pools and an asynchronous map function.
General Audience
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The course finishes with advice on how to write robust asynchronous code, and how to test and debug it.
What You Will Learn
- Take advantage of multi threading (concurrency) using the threading module
- Implement multi processing (parallelism) using the multiprocessing module
- Understand inter-process communication and data sharing using locks, queues, semaphores, barriers, events, and timers
- Test and debug asynchronous Python code
- Understand thread and process pools using concurrent.futures
Who This Video Is For
Intermediate to experienced Python programmers who want to speed up existing Python code by using multi processing and multi threading.
Field of Study
Science
Content Type
Instructional material
Contributor
Springer Nature
Date Published / Released
2021
Publisher
Springer Nature
Series
SN Video Coding and Web Development
Copyright Message
Copyright © 2021 Springer Nature
×
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SN Video Coding and Web Development, Blazor Performance
presented by Peter Himschoot; produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 13 mins
This video presents patterns and practices for making Blazor WebAssembly applications run fast at their optimal speeds. You’ll learn patterns and best practices for performance that broadly cover two aspects of Blazor development: Firstly, you’ll learn about optimizing application download size so your applica...
Sample
presented by Peter Himschoot; produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 13 mins
Description
This video presents patterns and practices for making Blazor WebAssembly applications run fast at their optimal speeds. You’ll learn patterns and best practices for performance that broadly cover two aspects of Blazor development: Firstly, you’ll learn about optimizing application download size so your applications load and execute without undue lag. Secondly, you’ll learn techniques for reducing the number of updates that Blazor needs to a...
This video presents patterns and practices for making Blazor WebAssembly applications run fast at their optimal speeds. You’ll learn patterns and best practices for performance that broadly cover two aspects of Blazor development: Firstly, you’ll learn about optimizing application download size so your applications load and execute without undue lag. Secondly, you’ll learn techniques for reducing the number of updates that Blazor needs to apply to user interface elements in the browser’s DOM. While Blazor WebAssembly is already designed to provide strong performance, the techniques in this video provide you additional tools and techniques that you can employ in order to delight your users and gain a competitive edge against your competition. Techniques taught in this video such as assembly trimming, lazy loading, and reducing unnecessary user interface updates can put your applications a step ahead of your competition when it comes to creating fast applications that users will want to return to again and again. What You Will Learn: Work with lazy-load assemblies to incrementally download your application’s code; Use virtualization to avoid rendering UI elements not currently visible; Create lightweight components that reduce unnecessary UI updates; Reduce unnecessary rendering of component subtrees; Optimize JavaScript interop speeds; Limit the number of components and reduce frequent events. Who This Video Is For: Blazor developers who have experience building WebAssembly applications. Especially for those developers who want to gain an edge on their competition through faster application performance.
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Field of Study
Science
Content Type
Instructional material
Contributor
Springer Nature
Author / Creator
Peter Himschoot
Date Published / Released
2021
Publisher
Springer Nature
Series
SN Video Coding and Web Development
Topic / Theme
Internet, Software engineering, Computer software
Copyright Message
Copyright © 2021 Springer Nature B.V.
×
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SN Video Coding and Web Development, Getting Started with Databricks
presented by Robert Ilijason; produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 1 mins
A hit-the-ground-running introduction to the expansive world of understanding your data. If you fail to understand what all the hoopla is around data analytics, but you know you need to jump in somewhere, this video is for you. Learning a handful of techniques you’ll get started with turning great quantities of...
Sample
presented by Robert Ilijason; produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 1 mins
Description
A hit-the-ground-running introduction to the expansive world of understanding your data. If you fail to understand what all the hoopla is around data analytics, but you know you need to jump in somewhere, this video is for you. Learning a handful of techniques you’ll get started with turning great quantities of data into useful information that can help you better understand your own world of data.This video is a user-friendly entry point that...
A hit-the-ground-running introduction to the expansive world of understanding your data. If you fail to understand what all the hoopla is around data analytics, but you know you need to jump in somewhere, this video is for you. Learning a handful of techniques you’ll get started with turning great quantities of data into useful information that can help you better understand your own world of data.This video is a user-friendly entry point that teaches you how to use machine learning algorithms to move from the trenches of data to making informed data-driven decisions. It begins with a brief introduction to data analysis and guidance on how to set up a development environment in Databricks. From there you will investigate data using popular tools such as SQL and Python on top of Apache Spark. From there you will move on to learning how to clean up data and prepare it for use in a machine-learning algorithm. The final segment offer guidance for presenting the results visually.What You Will Learn: Investigate small and large datasets using SQL and Python; Clean and prepare data for advanced analytics; Run machine learning algorithms; Show data results using visualizations. Who This Video Is For: People who work with data, including analysts, data engineers, data scientists, and the data curious. Basic knowledge about the data field is assumed, but no special skills are needed.
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Field of Study
Science
Content Type
Instructional material
Contributor
Springer Nature
Author / Creator
Robert Ilijason
Date Published / Released
2021
Publisher
Springer Nature
Series
SN Video Coding and Web Development
Topic / Theme
Software engineering, Computer software
Copyright Message
Copyright © 2021 Springer Nature B.V.
×
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SN Video Coding and Web Development, Performance Optimization for OLAP Workloads in SQL Server
presented by Edward Pollack; produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 2 mins
This video helps you to design databases and processes that can manage millions or billions (or more) rows of data for analytics and reporting. Analytic workloads follow different architectural and query patterns than standard transactional systems, and the associated data stores grow differently over time as comp...
Sample
presented by Edward Pollack; produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 2 mins
Description
This video helps you to design databases and processes that can manage millions or billions (or more) rows of data for analytics and reporting. Analytic workloads follow different architectural and query patterns than standard transactional systems, and the associated data stores grow differently over time as compared to online transaction-processing systems. This video covers the purpose, usage, and effects of growth on analytic data stores with...
This video helps you to design databases and processes that can manage millions or billions (or more) rows of data for analytics and reporting. Analytic workloads follow different architectural and query patterns than standard transactional systems, and the associated data stores grow differently over time as compared to online transaction-processing systems. This video covers the purpose, usage, and effects of growth on analytic data stores with a focus on storing the data directly in SQL Server. Topics covered in this video include design issues such as compression and advanced indexing techniques for online analytical processing (OLAP) data. Also covered are performance optimization and efficient query patterns. Watching this video helps you learn to better retrieve data with the ultimate goal of answering organizational questions using your analytics data store. What You Will Learn: Understand the nature of OLAP data, its structure, and its usage; Store OLAP data natively in SQL Server using best-practice options; Minimize resource consumption by compressing your data; Enhance query performance through columnstore indexing; Scale your OLAP query performance to account for future growth; Add new data elements to existing analytic data stores. Who This Video Is For: For data and analytics professionals who create, manage, and query large analytic data stores. For database professionals who expect to do so in the future, who want to know the options that are available in Microsoft SQL Server.
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Field of Study
Science
Content Type
Instructional material
Contributor
Springer Nature
Author / Creator
Edward Pollack
Date Published / Released
2021
Publisher
Springer Nature
Series
SN Video Coding and Web Development
Topic / Theme
Software engineering, Computer software
Copyright Message
Copyright © 2021 Springer Nature B.V.
×
See details
SN Video Coding and Web Development, Scaled Forecasting with Python and R
presented by Michael Keith; produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 21 mins
In this video, you will explore forecasting techniques in Python, including how to use machine learning models from Scikit Learn, as well as integrating R as a sub process to gain access to the robust forecast library, incorporating the auto.arima and tbats models. We create our own Python class called Forecaster...
Sample
presented by Michael Keith; produced by Springer Nature, in SN Video Coding and Web Development (London, England: Springer Nature, 2021), 1 hour 21 mins
Description
In this video, you will explore forecasting techniques in Python, including how to use machine learning models from Scikit Learn, as well as integrating R as a sub process to gain access to the robust forecast library, incorporating the auto.arima and tbats models. We create our own Python class called Forecaster that stores all of the relevant information about the predictions, including error metrics, model forms, hyperparameter selections, and...
In this video, you will explore forecasting techniques in Python, including how to use machine learning models from Scikit Learn, as well as integrating R as a sub process to gain access to the robust forecast library, incorporating the auto.arima and tbats models. We create our own Python class called Forecaster that stores all of the relevant information about the predictions, including error metrics, model forms, hyperparameter selections, and the forecasts themselves. The module is written in such a way that models can easily be added to the framework for increasing accuracy. In the specific scenario, we are forecasting on 150 separate time series—3 time series for each state using economic indicators from the St. Louis Federal Reserve (FRED) website. We build an API to extract the data and add our own indicators that we think will influence the future. At the time that this video is published, the country is experiencing a recession from the COVID-19 pandemic. Therefore, we will extract the recession indicator from the FRED website and make assumptions about how long the recession will last. Using this information, we create an economic outlook for each state. These forecasts can then be used to see which states we expect to recover most quickly from the recession. This modeling process will be done in Python on a Jupyter Notebook, so it's a good idea to have Anaconda installed on your computer so you can follow along. The rpy2 library from Python will be utilized, so having R installed in your local environment is necessary. What You Will Learn: Create classes in Python; Make predictions with machine learning and common forecasting models; Manipulate data with Pandas; Store data in base Python structures (lists, dictionaries); Create APIs and visual aids. Who This Book Is For: Data scientists with experience who are looking to take their forecasting skills to the next level.
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Field of Study
Science
Content Type
Instructional material
Contributor
Springer Nature
Author / Creator
Michael Keith
Date Published / Released
2021
Publisher
Springer Nature
Series
SN Video Coding and Web Development
Topic / Theme
Software engineering, Computer software
Copyright Message
Copyright © 2021 Springer Nature B.V.
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Animal Impossible, Season 1, Episode 1, Cats
directed by John Shackleton, fl. 2004; presented by Adam Gendle, fl. 2009 and Tim Warwood, fl. 2009; produced by John Shackleton, fl. 2004, BBC Studios, in Animal Impossible, Season 1, Episode 1 (London, England: BBC Worldwide, 2020), 50 mins
Incredible facts and myths about the animal kingdom are put to the test. This time... Is it true that cats always land on their feet?
Sample
directed by John Shackleton, fl. 2004; presented by Adam Gendle, fl. 2009 and Tim Warwood, fl. 2009; produced by John Shackleton, fl. 2004, BBC Studios, in Animal Impossible, Season 1, Episode 1 (London, England: BBC Worldwide, 2020), 50 mins
Description
Incredible facts and myths about the animal kingdom are put to the test. This time... Is it true that cats always land on their feet?
Field of Study
Science
Content Type
Documentary
Contributor
John Shackleton, fl. 2004, BBC Studios
Author / Creator
John Shackleton, fl. 2004, Adam Gendle, fl. 2009, Tim Warwood, fl. 2009
Date Published / Released
2020
Publisher
BBC Worldwide
Series
Animal Impossible
Topic / Theme
Wild cats, Cats
Copyright Message
Copyright © 2019 BBC Worldwide
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