- Many developers were attracted to Angular.js because it was built by Google which gave Angular.js automatic credibility.At about the same time, Web Components specification promised to make it possible for developers to create reusable widgets that were isolated from their context and were easy to compose with other widgets.The Web Components specification was four separate specifications that worked together.HTML Template — provides HTML markup for the componentCustom Element — provides a mechanism to create a custom HTML elementShadow DOM — isolates the internals of the component from the context that rendered itHTML Import — makes it possible to load the Web Component into a pageA team at Google created a polyfill library that provided Web Components for all browsers at the time.
- The Ember.js team looked at large Backbone applications to find similarities.They identified the need to render nested views where some parts of the application where consistent while other parts changed from one part of the app to another.They also saw the URL as a key player in the architecture of web applications.
- Some brave Backbone developers were adding React as views to their applications to fix performance problems that they were encountering.In response to the threat posed by React, the Ember core team created a plan to adopt ideas introduced by React into the Ember framework.
- They recognized the need for backward compatibility and created an upgrade path that allowed existing applications to upgrade to a version of Ember that included a new React-inspired rendering engine.Over the course of 4 minor releases Ember.js deprecated Views, moved the community to a CLI-based build process and made component-based architecture the foundation of Ember application development.
- The Angular team calls the new framework a platform because they plan to provide everything that a professional developer needs to build web applications.
There’s been a lot of development in the frontend frameworks ecosystem over the last seven years. We’ve learned a lot about what it takes to build and maintain large applications. We’ve seen many new…
Continue reading “Choosing a frontend framework in 2017 – This Dot Labs – Medium”
- I’ve created two nearly identical sample applications, one in Vue and one in React, if you’d like to give either framework a shot in the context of the samples in this article.
- Both React and Vue are focused solely on the UI layer, and leave functionality such as routing and state handling to companion frameworks.
- In Vue there’s no need to call a state management function like , as the data parameter on the Vue object acts as the holder for application data.
- We deploy a mixture of consulting, staff augmentation, and training to level up teams and solve engineering problems.
- Whether it’s transitioning walmart.com to React, moving speedtest.net off Flash, or helping a startup build and scale an MVP, we’re ready to help teams of any size.
A collection of composable React components for building interactive data visualizations.
Continue reading “React.js components formodular charting and data visualization”
- The React project originated at Facebook; the open source project has a BSD license and a patent rights grant from Facebook.
- It has no support for models or controllers, although there are projects related to React that cover these functions, along with routing.
- You can easily combine React with other architectures.
Learn the key concepts behind React and how to use JSX elements and components to build lean and fast web front ends
Continue reading “Get started with React: The InfoWorld tutorial”
- Zaha: a visual note taking appDemo: is a simple visual note taker / mood board made with React.You create your visual notes by adding a mix of text or image notes on to the board.
- You can drag around each text/image note around the board or edit it’s color highlight for more organizing within the board.This project is mostly based on how I come up with ideas for projects or crystalize concepts in my head.
- I usually collect inspiration images and texts (mostly quotes) to get me started with an idea.
- My sketchbook is full of bits and pieces of ideas in the form of nuggets of texts and images.This is v1 and you can’t save your notes yet!
- ( coming soon 🙂 )This project is made at the Recurse Center , where I am currently learning programming.I called the project Zaha — after one of my favorite architect!
“Zaha: a visual note taking app” is published by Tamrat
Continue reading “Zaha: a visual note taking app – Tamrat – Medium”
- While this is a subject of some debate these days, our experiments placing BN after activation on small networks failed to converge as well.To optimize the network we used Cyclical Learning Rates and (fellow student) Brad Kenstlerâs excellent Keras implementation.
- This was hard to defend against as a) there just arenât that many photographs of hotdogs in soft focus (we get hungry just thinking about it) and b) it could be damaging to spend too much of our networkâs capacity training for soft focus, when realistically most images taken with a mobile phone will not have that feature.
- Of the remaining 147k images, most were of food, with just 3k photos of non-food items, to help the network generalize a bit more and not get tricked into seeing a hotdog if presented with an image of a human in a red outfit.Our data augmentation rules were as follows:We applied rotations within Âą135 degreesâââsignificantly more than average, because we coded the application to disregard phone orientation.Height and width shifts of 20%Shear range of 30%Zoom range of 10%Channel shifts of 20%Random horizontal flips to help the network generalizeThese numbers were derived intuitively, based on experiments and our understanding of the real-life usage of our app, as opposed to careful experimentation.The final key to our data pipeline was using Patrick Rodriguezâs multiprocess image data generator for Keras.
- Phase 2 ran for 64 more epochs (4 CLR cycles with a step size of 8 epochs), with a learning rate between 0.0004 and 0.0045, on a triangular 2 policy.Phase 3 ran for 64 more epochs (4 CLR cycles with a step size of 8 epochs), with a learning rate between 0.000015 and 0.0002, on a triangular 2 policy.While learning rates were identified by running the linear experiment recommended by the CLR paper, they seem to intuitively make sense, in that the max for each phase is within a factor of 2 of the previous minimum, which is aligned with the industry standard recommendation of halving your learning rate if your accuracy plateaus during training.We were able to perform some runs on a Paperspace P5000 instance in the interest of time.
- In those cases, we were able to double the batch size, and found that optimal learning rates for each phase were roughly double as well.Running Neural Networks on MobileÂ PhonesEven having designed a relatively compact neural architecture, and having trained it to handle situations it may find in a mobile context, we had a lot of work left
How Silicon Valley build the real AI app that identifies hotdogs — and not hotdogs using mobile TensorFlow, Keras & React Native.
Continue reading “How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native”