Posts
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yii2-export
A library to export server/db data in various formats (e.g. excel, html, pdf, csv etc.)
You can see detailed documentation and demonstration on usage of the extension.
Tags: #php • spreadsheet • yii2
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magpie
Deep neural network framework for multi-label text classification
>>> magpie = Magpie() >>> magpie.init_word_vectors('/path/to/corpus', vec_dim=100) >>> magpie.train('/path/to/corpus', ['label1', 'label2', 'label3'], epochs=3) Training... >>> magpie.predict_from_text('Well, that was quick!') [('label1', 0.96), ('label3', 0.65), ('label2', 0.21)]Tags: #python • neural-network • nlp
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Generamba
This codegenerator is too brilliant to be real!
- Supports work with .xcodeproj files out of the box. All generated class files are automatically placed to specific folders and groups of Xcode project.
- Can generate both code itself and tests adding them to right targets.
- Based on work with liquid-templates that have plain and readable syntax in comparison with templates for Xcode.
- It is very easy to create a new module:
generamba gen [MODULE_NAME] [TEMPLATE_NAME]. You do not need to input a bunch of data each time because each project corresponds to only one configuration file that holds standard file system and Xcode-project pathes, names of targets, information about the author.
Tags: #ruby
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ballerina-lang
The Ballerina Programming Language
Ballerina is an open source programming language and platform for cloud-era application programmers to easily write software that just works.
Tags: #java • programming-language • language
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graphql-modules
Enterprise Grade Tooling For Your GraphQL Server
- Reusable modules - Modules are defined by their GraphQL schema (Schema first design). They can be shared between apps.
- Scalable structure - Manage multiple teams and features, multiple micro-services and servers.
- Gradual growth - A clear, gradual path from a very simple and fast, single-file modules, to scalable multi-file, multi-teams, multi-repo, multi-server modules.
- Testable - A rich toolset around testing, mocking and separation.
Tags: #typescript • graphql • graphql-server
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reinforcement-learning-car
Using reinforcement learning to teach a car to avoid obstacles.
NOTE: If you’re coming here from parts 1 or 2 of the Medium posts, you want to visit the releases section and check out version 1.0.0, as the code has evolved passed that.
This is a hobby project I created to learn the basics of reinforcement learning. It uses Python3, Pygame, Pymunk, Keras and Theanos. It employes a Q-learning (unsupervised) algorithm to learn how to move an object around a screen (drive itself) without running into obstacles.
The purpose of this project is to eventually use the learnings from the game to operate a real-life remote-control car, using distance sensors. I am carrying on that project in another GitHub repo here: https://github.com/harvitronix/rl-rc-car
This version of the code attempts to simulate the use of sensors to get us a step closer to being able to use this in the real world.
Full writeups that pertain to version 1.0.0 can be found here:
Part 1: https://medium.com/@harvitronix/using-reinforcement-learning-in-python-to-teach-a-virtual-car-to-avoid-obstacles-6e782cc7d4c6
Part 2: https://medium.com/@harvitronix/reinforcement-learning-in-python-to-teach-a-virtual-car-to-avoid-obstacles-part-2-93e614fcd238#.vbakopk4o
Part 3 (for this version of the code): https://medium.com/@harvitronix/reinforcement-learning-in-python-to-teach-an-rc-car-to-avoid-obstacles-part-3-a1d063ac962f
Tags: #python
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DeepLabCut
Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals
Very quick start:
pip install deeplabcut- you also need tensorflow and wxPython see here
Tags: #python • behavior-analysis • deep-learning
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sketch-material
Sketch material is a sketch plugin that will help you generate complex material components like tables, chips, forms etc…
Sketch material is a sketch plugin that will help you generate complex material components like tables, chips, forms etc…
Tags: #javascript
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imitation
Clean PyTorch implementations of imitation learning algorithms
This project aims to provide clean implementations of imitation learning algorithms. Currently we have implementations of Behavioral Cloning, DAgger (with synthetic examples), Adversarial Inverse Reinforcement Learning, and Generative Adversarial Imitation Learning.
Tags: #python • reward-learning • inverse-reinforcement-learning
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jSignature
jQuery plugin for adding web signature functionality
jSignature is a jQuery plugin which simplifies creation of a signature capture field in the browser window, allowing a user to draw a signature using mouse, pen, or finger.
jSignature captures signature as vector outlines of the strokes. Although jSignature can export great bitmap (PNG) too, extraction of highly scalable stroke movement coordinates (aka vector image) of the signature allows much greater flexibility of signature rendering.
A extra effort (through smoothing and pressure simulation) is made to make the strokes look pretty on the screen while these are drawn by the signor.
All major desktop, tablet and phone browsers are supported. HTML5 Canvas element is used by default. We fall back on Flash-based Canvas element emulator (FlashCanvas) when actual Canvas is not supported by the browser (Internet Explorer v.8 and lower).
Real-time jSignature renders only the device-appropriate “prettiest” approximation of what we capture. Capture of data is always same - we capture as many movement coordinates as possible. Rendering of strokes differs per browser’s capabilities, efficiency of the device, screen size.
This degrading and enhancing of screen representation of the captured signature is done on purpose to insure that rendering does not impead on the responsiveness of capture. For example, on slow rendering devices (Android Browser, FlashCanvas-based Canvas emulation) smoothing is kicked up a notch to compensate for large gaps in captured stroke coordinates - a result of inefficiency of capture device. In all cases, customer shold be pleased by responsiveness and beauty of the drawing.
jSignature makes it easy to pluck itself into an existing styled site. jSignature automatically detects the colors used on the wrapping element (text color = pen color, background = background) and auto-picks a pleasing middle-shade for ‘decor’ (signature line). jSignature adapts well to fixed and variable width web page designs, and various size screens (phones, tablets, computer screens) and automatically rescales the drawing area and signature when parent element changes size.
See demos here.
Tags: #javascript
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