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Originally published on January 1, 2022. Charlie Gerard is an incredibly productive developer.  In addition to being the author of Practical Machine Learning in JavaScript, her website charliegerard.dev has a long list of really interesting side projects exploring the intersection of human computer interaction, computer vision, interactivity, and art.  In this episode we touch on

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Mikeal Rogers, Alex Sexton, and Rachel White discuss VM Neutrality in Node.js, learning JavaScript, and Mastodon - the open source friendly alternative to Twitter, et al.

Mikeal Rogers, Alex Sexton, and Rachel White discuss VM Neutrality in Node.js, learning JavaScript, and Mastodon - the open source friendly alternative to Twitter, et al.

Mikeal Rogers, Alex Sexton, and Rachel White discuss VM Neutrality in Node.js, learning JavaScript, and Mastodon - the open source friendly alternative to Twitter, et al.

Video content can be found here: https://www.youtube.com/channel/UC0BAd8tPlDqFvDYBemHcQPQ/

JavaScript Remote Conf 2020 May 13th to 15th - register now! Matt Crook joins the conversation to talk with the JavaScript Jabber panel to talk about his experience going through Nashville Software School. The panel discusses and asks questions about getting into programming, working through the bootcamp, and what prospects are for bootcamp graduates. Panel ? AJ O’Neal ? Aimee Knight ? Charles Max Wood ? Steve Edwards ? Dan Shappir Guest ? Matt Crook Sponsors ? Taiko ? Educative.io | Click here for 10% discount "The MaxCoders Guide to Finding Your Dream Developer Job" by Charles Max Wood is now available on Amazon. Get Your Copy Today!   Picks AJ O’Neal: ? PostgREST ? The Way of Kings ? VirtualBox ? Bootable Installers for MacOS, Windows, and more Aimee Knight: ? State of Microservices 2020 Report ? Peloton Bike Charles Max Wood: ? The Hobbit ? D&D Starter Set ? JavaScript Weekly ? Devchat.tv Remote Meetups ? Devchat.tv Remote Conferences ? Reading to Kids Steve Edwards: ? It Is Well With My Soul ? Pitbull Gold PRO Skull Shaver ? Brad Balfour Dan Shappir: ? Gödel, Escher, Bach ? Translating "The Hobbit" in Captivity Matt Crook: ? Follow Matt on Twitter > @mgcrook , Instagram , LinkedIn ? Swolenormous ? Fireship ? Static Headz ? Yugen Follow JavaScript Jabber on Twitter > @JSJabber

Tracy Lee joined the show to talk about bringing people together, helping people, and making an impact. We covered learning JavaScript, the ins and outs of her road to get to where she's at today, hitting burnout and sleeping for two weeks, breaking into the JavaScript community, and the fun cruise, workshops, and conferences she's working on for the JavaScript community.

Video content can be found here: https://www.youtube.com/channel/UC0BAd8tPlDqFvDYBemHcQPQ/

Video content can be found here: https://www.youtube.com/channel/UC0BAd8tPlDqFvDYBemHcQPQ/

Video content can be found here: https://www.youtube.com/channel/UC0BAd8tPlDqFvDYBemHcQPQ/

Video content can be found here: https://www.youtube.com/channel/UC0BAd8tPlDqFvDYBemHcQPQ/

Video content can be found here: https://www.youtube.com/channel/UC0BAd8tPlDqFvDYBemHcQPQ/

Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called "Sashimi" that allows any computer to be used as a distribution node only by accessing a website. We have also developed a new JavaScript neural network framework called "Sukiyaki" that uses general purpose GPUs with web browsers. Sukiyaki performs 30 times faster than a conventional JavaScript library for deep convolutional neural networks (deep CNNs) learning. The combination of Sashimi and Sukiyaki, as well as new distribution algorithms, demonstrates the distributed deep learning of deep CNNs only with web browsers on various devices. The libraries that comprise the proposed methods are available under MIT license at http://mil-tokyo.github.io/.

Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called "Sashimi" that allows any computer to be used as a distribution node only by accessing a website. We have also developed a new JavaScript neural network framework called "Sukiyaki" that uses general purpose GPUs with web browsers. Sukiyaki performs 30 times faster than a conventional JavaScript library for deep convolutional neural networks (deep CNNs) learning. The combination of Sashimi and Sukiyaki, as well as new distribution algorithms, demonstrates the distributed deep learning of deep CNNs only with web browsers on various devices. The libraries that comprise the proposed methods are available under MIT license at http://mil-tokyo.github.io/.

Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called "Sashimi" that allows any computer to be used as a distribution node only by accessing a website. We have also developed a new JavaScript neural network framework called "Sukiyaki" that uses general purpose GPUs with web browsers. Sukiyaki performs 30 times faster than a conventional JavaScript library for deep convolutional neural networks (deep CNNs) learning. The combination of Sashimi and Sukiyaki, as well as new distribution algorithms, demonstrates the distributed deep learning of deep CNNs only with web browsers on various devices. The libraries that comprise the proposed methods are available under MIT license at http://mil-tokyo.github.io/.

Panel?Noel Rappin (twitter github blog)?Jamison Dance (twitter github blog)?Charles Max Wood (twitter github Teach Me To Code Intro to CoffeeScript)?AJ O'Neal (twitter github blog)Discussion00:52 - Works in training and talent development for Groupon00:56 - Author of Rails Test Prescriptions and upcoming Master Space and Time with JavaScript01:21 - Writing a book about JavaScript02:33 - Focus of the book?Part 1: Jasmine and jQuery and the JavaScript Object Model?Part 2: Extended examples of jQuery?Part 3: Backbone?Part 4: Ember03:46 - Self-published authors05:15 - Approaches and mindsets to learning JavaScript06:04 - \"Gotchas!\" and bad features in Javascript09:17 - Modeling JavaScript for beginners11:23 - (AJ joins the podcast)11:42 - Resources/Classes for learning JavaScript?Good Parts Book: Douglas Crockford?JavaScript Patterns: Stoyan Stefanov?Eloquent JavaScript: A Modern Introduction to Programming: Marijn Haverbeke?Maintainable JavaScript: Nicholas C. Zakas13:54 - Hiring people with JavaScript experience at Groupon15:12 - Training workshops17:00 - Getting new hires up to speed quickly?Pairing?Mentoring?Lectures?Workshops21:38 - Book Learning?You can learn at your own pace?But it's hard to ask questions to a book22:51 - How Noel gained expertise in JavaScript24:38 - Code reading and learning to program a language26:18 - Teaching people JavaScript as their very first language31:55 - Classroom layout33:42 - Online training?Kahn Academy Computer Science?Code Academy?Starter League40:00 - Finding a mentor?Stack OverflowPicks?Shrines by Purity Ring (Jamison)?Learnable Programming: Bret Victor (Jamison)?Mob Software: Richard P. Gabriel & Ron Goldman (Jamison)?Monoprice.com (AJ)?ZREO: Zelda Reorchestrated (AJ)?The Official Twitter App (Chuck)?Fluid App (Chuck)?Try Jasmine! (Noel)?Justin Searls (Noel)?The Atrocity Archives: Charles Stross (Noel)?Futurity: A Musical by The Lisps (Noel)TranscriptNOEL: I'm trying to figure out where the chat is in this stupid Skype interface.JAMISON: Just imagine the worst place it could possibly be and that's where it is.[This episode is sponsored by ComponentOne, makers of Wijmo. If you need stunning UI elements or awesome graphs and charts, then go to wijmo.com and check them out.][Hosting and bandwidth provided by The Blue Box Group. Check them out at bluebox.net]CHUCK: Hey everybody and welcome to Episode 30 of the JavaScript Jabber show! This week on our panel we have, Jamison Dance.JAMISON: Hey guys!CHUCK: I'm Charles Max Wood from devchat.tv and this week, we have a special guest and that's Noel Rappin!NOEL: Hey everybody!CHUCK: For the people who don't know who you are, you want to introduce yourself, Noel?NOEL: Sure. I currently work in training and talent development for Groupon. And I am the author of previously \"Rails Test Prescriptions\" and currently a self-published book called \"Master Time and Space with JavaScript\", which you can get at noelrappin.com. I need to spell that out, right? N-o-e-l-r-a-p-p-i-n.comCHUCK: So I'm little curious, before we get into the topic which is learning and teaching JavaScript, how did you get into writing a book about JavaScript? What's your background there?NOEL: You know, it actually relates to teaching and learning JavaScript. I think, I was like... a lot of long time web devs. I spent my first round as a web consultant in around, turn of the century 2000's. I spent time trying to talk clients out of JavaScript stuff because it was such a pain in the neck. And I kind of got away from it for awhile and came back a couple of years ago to realize that basically, everything had changed and they were actually usable tools now.And last summer, I was working with a... at that time,

Panel?Noel Rappin (twitter github blog)?Jamison Dance (twitter github blog)?Charles Max Wood (twitter github Teach Me To Code Intro to CoffeeScript)?AJ O'Neal (twitter github blog)Discussion00:52 - Works in training and talent development for Groupon00:56 - Author of Rails Test Prescriptions and upcoming Master Space and Time with JavaScript01:21 - Writing a book about JavaScript02:33 - Focus of the book?Part 1: Jasmine and jQuery and the JavaScript Object Model?Part 2: Extended examples of jQuery?Part 3: Backbone?Part 4: Ember03:46 - Self-published authors05:15 - Approaches and mindsets to learning JavaScript06:04 - \"Gotchas!\" and bad features in Javascript09:17 - Modeling JavaScript for beginners11:23 - (AJ joins the podcast)11:42 - Resources/Classes for learning JavaScript?Good Parts Book: Douglas Crockford?JavaScript Patterns: Stoyan Stefanov?Eloquent JavaScript: A Modern Introduction to Programming: Marijn Haverbeke?Maintainable JavaScript: Nicholas C. Zakas13:54 - Hiring people with JavaScript experience at Groupon15:12 - Training workshops17:00 - Getting new hires up to speed quickly?Pairing?Mentoring?Lectures?Workshops21:38 - Book Learning?You can learn at your own pace?But it's hard to ask questions to a book22:51 - How Noel gained expertise in JavaScript24:38 - Code reading and learning to program a language26:18 - Teaching people JavaScript as their very first language31:55 - Classroom layout33:42 - Online training?Kahn Academy Computer Science?Code Academy?Starter League40:00 - Finding a mentor?Stack OverflowPicks?Shrines by Purity Ring (Jamison)?Learnable Programming: Bret Victor (Jamison)?Mob Software: Richard P. Gabriel & Ron Goldman (Jamison)?Monoprice.com (AJ)?ZREO: Zelda Reorchestrated (AJ)?The Official Twitter App (Chuck)?Fluid App (Chuck)?Try Jasmine! (Noel)?Justin Searls (Noel)?The Atrocity Archives: Charles Stross (Noel)?Futurity: A Musical by The Lisps (Noel)TranscriptNOEL: I'm trying to figure out where the chat is in this stupid Skype interface.JAMISON: Just imagine the worst place it could possibly be and that's where it is.[This episode is sponsored by ComponentOne, makers of Wijmo. If you need stunning UI elements or awesome graphs and charts, then go to wijmo.com and check them out.][Hosting and bandwidth provided by The Blue Box Group. Check them out at bluebox.net]CHUCK: Hey everybody and welcome to Episode 30 of the JavaScript Jabber show! This week on our panel we have, Jamison Dance.JAMISON: Hey guys!CHUCK: I'm Charles Max Wood from devchat.tv and this week, we have a special guest and that's Noel Rappin!NOEL: Hey everybody!CHUCK: For the people who don't know who you are, you want to introduce yourself, Noel?NOEL: Sure. I currently work in training and talent development for Groupon. And I am the author of previously \"Rails Test Prescriptions\" and currently a self-published book called \"Master Time and Space with JavaScript\", which you can get at noelrappin.com. I need to spell that out, right? N-o-e-l-r-a-p-p-i-n.comCHUCK: So I'm little curious, before we get into the topic which is learning and teaching JavaScript, how did you get into writing a book about JavaScript? What's your background there?NOEL: You know, it actually relates to teaching and learning JavaScript. I think, I was like... a lot of long time web devs. I spent my first round as a web consultant in around, turn of the century 2000's. I spent time trying to talk clients out of JavaScript stuff because it was such a pain in the neck. And I kind of got away from it for awhile and came back a couple of years ago to realize that basically, everything had changed and they were actually usable tools now.And last summer, I was working with a... at that time,

A tiny list limited to the best JavaScript Learning Resources To restore the repository download the bundle micromata-awesome-javascript-learning_-_2017-06-12_22-49-28.bundle and run: git clone micromata-awesome-javascript-learning_-_2017-06-12_22-49-28.bundle -b master Source: https://github.com/micromata/awesome-javascript-learning Uploader: micromata Upload date: 2017-06-12

A tiny list limited to the best JavaScript Learning Resources To restore the repository download the bundle micromata-awesome-javascript-learning_-_2017-06-12_22-49-28.bundle and run: git clone micromata-awesome-javascript-learning_-_2017-06-12_22-49-28.bundle -b master Source: https://github.com/micromata/awesome-javascript-learning Uploader: micromata Upload date: 2017-06-12

Charlie Gerard is an incredibly productive developer. In addition to being the author of Practical Machine Learning in JavaScript, her website charliegerard.dev has a long list of really interesting side projects exploring the intersection of human computer interaction, computer vision, interactivity, and art. In this episode we touch on some of these projects and broadly

Check out  Newbie Remote Conf !   02:44 - What it Takes to Learn JavaScript in 2016 04:03 - Resources: Then vs Now 09:42 - Are there prerequisites? Should you have experience? 20:34 - Choosing What to Learn ? The iPhreaks Show Episode #153: Using Mobile Devices to Manage Diabetes with Scott Hanselman 28:19 - Deciding What to Learn Next 31:19 - Keeping Up: Obligations As a Developer 34:22 - Deciding What to Learn Next (Cont’d) 42:01 - Recommendations ? You-Dont-Know-JS ? gulp.js ? webpack ? The Little Schemer ? Designing Data-Intensive Applications by Martin Kleppmann   Picks ? accidentally nonblocking  (Jamison) ? choo  (Jamison) ? Web Rebels  (Jamison) ? React Rally  (Jamison) ? Grab The Gold  (Aimee) ? node-for-beginners  (Aimee) ? Procrastinate On Purpose by Rory Vaden  (Chuck) ? Newbie Remote Conf  (Chuck) ? Get A Coder Job  (Chuck)

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Panelists Suz Hinton and Nick Nisi discuss TensorFlow.js and Machine Learning in JavaScript with special guest Paige Bailey, TensorFlow mom and developer Advocate for Google AI.

Panelists Suz Hinton and Nick Nisi discuss TensorFlow.js and Machine Learning in JavaScript with special guest Paige Bailey, TensorFlow mom and developer Advocate for Google AI.

Panelists Suz Hinton and Nick Nisi discuss TensorFlow.js and Machine Learning in JavaScript with special guest Paige Bailey, TensorFlow mom and developer Advocate for Google AI.

Panelists Suz Hinton and Nick Nisi discuss TensorFlow.js and Machine Learning in JavaScript with special guest Paige Bailey, TensorFlow mom and developer Advocate for Google AI.

Learning Php Mysql Javascript Css Html 5 Robin Nixon 3e Urduinpage.com

The first data structure that first-year undergraduate students learn during the programming and algorithms courses is the one-dimensional array. For novice programmers, it might be hard to understand different algorithms on arrays (e.g. searching, mirroring, sorting algorithms), because the algorithms dynamically change the values of elements. In these situations, visualizations and animations might be helpful didactic tools. In this paper, we briefly overview animations with different views and introduce our Javascript library for developing interactive micro-level animations. Using this library, different sorting algorithm animations were created, which were used in a pedagogical experiment. The results showed that our interactive animations helped students to understand the visualized sorting algorithms.

MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript. Especially, our matrix multiplication is 177 times faster than the fastest JavaScript benchmark. Based on Sushi, a machine learning library called Tempura is provided, which supports various algorithms widely used in machine learning research. We also provide Soba as a visualization library. The implementations of our libraries are clearly written, properly documented and thus can are easy to get started with, as long as there is a web browser. These libraries are available from http://mil-tokyo.github.io/ under the MIT license.

MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript. Especially, our matrix multiplication is 177 times faster than the fastest JavaScript benchmark. Based on Sushi, a machine learning library called Tempura is provided, which supports various algorithms widely used in machine learning research. We also provide Soba as a visualization library. The implementations of our libraries are clearly written, properly documented and thus can are easy to get started with, as long as there is a web browser. These libraries are available from http://mil-tokyo.github.io/ under the MIT license.

MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript. Especially, our matrix multiplication is 177 times faster than the fastest JavaScript benchmark. Based on Sushi, a machine learning library called Tempura is provided, which supports various algorithms widely used in machine learning research. We also provide Soba as a visualization library. The implementations of our libraries are clearly written, properly documented and thus can are easy to get started with, as long as there is a web browser. These libraries are available from http://mil-tokyo.github.io/ under the MIT license.

MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript. Especially, our matrix multiplication is 177 times faster than the fastest JavaScript benchmark. Based on Sushi, a machine learning library called Tempura is provided, which supports various algorithms widely used in machine learning research. We also provide Soba as a visualization library. The implementations of our libraries are clearly written, properly documented and thus can are easy to get started with, as long as there is a web browser. These libraries are available from http://mil-tokyo.github.io/ under the MIT license.

MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript. Especially, our matrix multiplication is 177 times faster than the fastest JavaScript benchmark. Based on Sushi, a machine learning library called Tempura is provided, which supports various algorithms widely used in machine learning research. We also provide Soba as a visualization library. The implementations of our libraries are clearly written, properly documented and thus can are easy to get started with, as long as there is a web browser. These libraries are available from http://mil-tokyo.github.io/ under the MIT license.

Deep learning is increasingly attracting attention for processing big data. Existing frameworks for deep learning must be set up to specialized computer systems. Gaining sufficient computing resources therefore entails high costs of deployment and maintenance. In this work, we implement a matrix library and deep learning framework that uses JavaScript. It can run on web browsers operating on ordinary personal computers and smartphones. Using JavaScript, deep learning can be accomplished in widely diverse environments without the necessity for software installation. Using GPGPU from WebCL framework, our framework can train large scale convolutional neural networks such as VGGNet and ResNet. In the experiments, we demonstrate their practicality by training VGGNet in a distributed manner using web browsers as the client.

How-to textbook for HTML, CSS & Javascript

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