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In this interview Andre Videla discusses how the swift program language encourages programming in a total style. He also discusses his love of Idris, proof assistants and how his research into haskell, idris and dependant types have made him a better swift programmer.

In this interview Andre Videla discusses how the swift program language encourages programming in a total style. He also discusses his love of Idris, proof assistants and how his research into haskell, idris and dependant types have made him a better swift programmer.

In this interview Andre Videla discusses how the swift program language encourages programming in a total style. He also discusses his love of Idris, proof assistants and how his research into haskell, idris and dependant types have made him a better swift programmer.

Paris Buttfield-Addison https://linux.conf.au/schedule/30362/view_talk Swift is one of the most interesting modern languages that you'll encounter in your travels, and with its recent open-source release growing its relevance across myriad different communities, it's a language worth learning. Swift is great for traditional, imperative programming, but especially excels at functional programming. This session will explore the basics of functional programming with Swift 2.0 using examples. It operates under the assumption that you are familiar with iterative programming in a relatively modern, or heavily-used language, such as Python, Java, C#, Perl 6, or similar. We'll explore some of the things functional programming is good at, such as filtering, reducing numbers, indexing, and more, and we'll do it using Swift's powerful and easy-to-understand functional programming features. We'll also do it using Swift's really, really, really cool REPL (read-evaluate-print-loop) live programming environment, Playgrounds. Learn why Swift might be useful to you, how to use it for the basics of functional programming, and where to go next on your Swift adventure. We'll also briefly look at how to set it up under Linux, as well as popular open-source libraries to make functional programming even more exciting with Swift, such as Dollar.swift, Promissum, Result, and Swiftz.

Paris Buttfield-Addison https://linux.conf.au/schedule/30362/view_talk Swift is one of the most interesting modern languages that you'll encounter in your travels, and with its recent open-source release growing its relevance across myriad different communities, it's a language worth learning. Swift is great for traditional, imperative programming, but especially excels at functional programming. This session will explore the basics of functional programming with Swift 2.0 using examples. It operates under the assumption that you are familiar with iterative programming in a relatively modern, or heavily-used language, such as Python, Java, C#, Perl 6, or similar. We'll explore some of the things functional programming is good at, such as filtering, reducing numbers, indexing, and more, and we'll do it using Swift's powerful and easy-to-understand functional programming features. We'll also do it using Swift's really, really, really cool REPL (read-evaluate-print-loop) live programming environment, Playgrounds. Learn why Swift might be useful to you, how to use it for the basics of functional programming, and where to go next on your Swift adventure. We'll also briefly look at how to set it up under Linux, as well as popular open-source libraries to make functional programming even more exciting with Swift, such as Dollar.swift, Promissum, Result, and Swiftz.

Paris Buttfield-Addison https://linux.conf.au/schedule/30362/view_talk Swift is one of the most interesting modern languages that you'll encounter in your travels, and with its recent open-source release growing its relevance across myriad different communities, it's a language worth learning. Swift is great for traditional, imperative programming, but especially excels at functional programming. This session will explore the basics of functional programming with Swift 2.0 using examples. It operates under the assumption that you are familiar with iterative programming in a relatively modern, or heavily-used language, such as Python, Java, C#, Perl 6, or similar. We'll explore some of the things functional programming is good at, such as filtering, reducing numbers, indexing, and more, and we'll do it using Swift's powerful and easy-to-understand functional programming features. We'll also do it using Swift's really, really, really cool REPL (read-evaluate-print-loop) live programming environment, Playgrounds. Learn why Swift might be useful to you, how to use it for the basics of functional programming, and where to go next on your Swift adventure. We'll also briefly look at how to set it up under Linux, as well as popular open-source libraries to make functional programming even more exciting with Swift, such as Dollar.swift, Promissum, Result, and Swiftz.

Paris Buttfield-Addison https://linux.conf.au/schedule/30362/view_talk Swift is one of the most interesting modern languages that you'll encounter in your travels, and with its recent open-source release growing its relevance across myriad different communities, it's a language worth learning. Swift is great for traditional, imperative programming, but especially excels at functional programming. This session will explore the basics of functional programming with Swift 2.0 using examples. It operates under the assumption that you are familiar with iterative programming in a relatively modern, or heavily-used language, such as Python, Java, C#, Perl 6, or similar. We'll explore some of the things functional programming is good at, such as filtering, reducing numbers, indexing, and more, and we'll do it using Swift's powerful and easy-to-understand functional programming features. We'll also do it using Swift's really, really, really cool REPL (read-evaluate-print-loop) live programming environment, Playgrounds. Learn why Swift might be useful to you, how to use it for the basics of functional programming, and where to go next on your Swift adventure. We'll also briefly look at how to set it up under Linux, as well as popular open-source libraries to make functional programming even more exciting with Swift, such as Dollar.swift, Promissum, Result, and Swiftz.

Paris Buttfield-Addison https://linux.conf.au/schedule/30362/view_talk Swift is one of the most interesting modern languages that you'll encounter in your travels, and with its recent open-source release growing its relevance across myriad different communities, it's a language worth learning. Swift is great for traditional, imperative programming, but especially excels at functional programming. This session will explore the basics of functional programming with Swift 2.0 using examples. It operates under the assumption that you are familiar with iterative programming in a relatively modern, or heavily-used language, such as Python, Java, C#, Perl 6, or similar. We'll explore some of the things functional programming is good at, such as filtering, reducing numbers, indexing, and more, and we'll do it using Swift's powerful and easy-to-understand functional programming features. We'll also do it using Swift's really, really, really cool REPL (read-evaluate-print-loop) live programming environment, Playgrounds. Learn why Swift might be useful to you, how to use it for the basics of functional programming, and where to go next on your Swift adventure. We'll also briefly look at how to set it up under Linux, as well as popular open-source libraries to make functional programming even more exciting with Swift, such as Dollar.swift, Promissum, Result, and Swiftz.

Yeah. Definitely a fan of #thinBasic - there's something similar for #macintosh - I think it's swift Not sure. But same idea. #Programming -enHjmXpx1vA

Yeah. Definitely a fan of #thinBasic - there's something similar for #macintosh - I think it's swift Not sure. But same idea. #Programming -enHjmXpx1vA

PACKT PUBLISHING HOUSE

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

After watching this session you will be able to answer below questions: 1. What is POP( Protocol Oriented Programming ) in Swift ? 2. What are the differences between POP( Protocol Oriented Programming ) & OOPs (Object Oriented Programming).? 3. What is a POP( Protocol Oriented Programming ) Approach ? 4. Why and where we need to use POP( Protocol Oriented Programming ) rather than OOPs (Object Oriented Programming) ? If you are comfortable with these above questions then congratulations !! :) You crossed "Section 2 's point number 2.2.2” / "Section 14" . More learning Reference : https://developer.apple.com/videos/play/wwdc2015/408/ My Target :- To complete "Jr. iOS Application Engineer Course" with 14 Sections. Stay Connected , like share , subscribe for future update!! Go Slow, Speed will come automatically!! Mail Me: [email protected] Twitter https://twitter.com/DaspanAbhimanyu Facebook https://www.facebook.com/abhimanyu.daspan.7 Instagram https://www.instagram.com/ioscoderabhimanyudaspan/ Youtube https://www.youtube.com/channel/UCCq5Vo5uFoAtBwGc1NlKCOw?view_as=subscriber Github:- https://github.com/AbhimanyuForiOS LinkedIn https://www.linkedin.com/in/abhimanyu-singh-rathore-daspan-🇮🇳-77677098/ Source: https://www.youtube.com/watch?v=a_CWqg1bZlQ Uploader: iOSCoderAbhimanyuDaspan

After watching this session you will be able to answer below questions: 1. What is POP( Protocol Oriented Programming ) in Swift ? 2. What are the differences between POP( Protocol Oriented Programming ) & OOPs (Object Oriented Programming).? 3. What is a POP( Protocol Oriented Programming ) Approach ? 4. Why and where we need to use POP( Protocol Oriented Programming ) rather than OOPs (Object Oriented Programming) ? If you are comfortable with these above questions then congratulations !! :) You crossed "Section 2 's point number 2.2.2” / "Section 14" . More learning Reference : https://developer.apple.com/videos/play/wwdc2015/408/ My Target :- To complete "Jr. iOS Application Engineer Course" with 14 Sections. Stay Connected , like share , subscribe for future update!! Go Slow, Speed will come automatically!! Mail Me: [email protected] Twitter https://twitter.com/DaspanAbhimanyu Facebook https://www.facebook.com/abhimanyu.daspan.7 Instagram https://www.instagram.com/ioscoderabhimanyudaspan/ Youtube https://www.youtube.com/channel/UCCq5Vo5uFoAtBwGc1NlKCOw?view_as=subscriber Github:- https://github.com/AbhimanyuForiOS LinkedIn https://www.linkedin.com/in/abhimanyu-singh-rathore-daspan-🇮🇳-77677098/ Source: https://www.youtube.com/watch?v=a_CWqg1bZlQ Uploader: iOSCoderAbhimanyuDaspan