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This is a Pharo library for partitioning a collection. Given a set of K proportions, for example 50%, 30%, and 20%, it shuffles the collection and divides it into K non-empty subsets in such a way that every element is included in exactly one subset.
RandomPartitioner can be used in machine learning and statistical analysis for splitting the data into training, validation, and test (a.k.a. holdout) sets, or partitioning the data for cross-validation.
A lightweight library for neural network graphs and training metrics for PyTorch, Tensorflow, and Keras. HiddenLayer is simple, easy to extend, and works great with Jupyter Notebook. It's not intended to replace advanced tools, such as TensorBoard, but rather for cases where advanced tools are too big for the task. HiddenLayer was written by Waleed Abdulla and Phil Ferriere, and is licensed under the MIT License.
A small library to get a notification when your training is complete or when it crashes during the process with two additional lines of code. When training deep learning models, it is common to use early stopping. Apart from a rough estimate, it is difficult to predict when the training will finish. Thus, it can be interesting to set up automatic notifications for your training. It is also interesting to be notified when your training crashes in the middle of the process for unexpected reasons.
Vikos is a library for supervised training of parameterized, regression, and classification models Design Goals
ai_lab is a library for loading datasets, data augmentation, training management, experiment management and result documentation
aug = Augment2D( blur=0.1, pixel_shift=0.1, rotate_hard=True, rotate_soft=False, noise=0.01, zoom=0.2, lab_shift=0.5, flip=True, contrast=0.5)
data_in,labelsin = [im for in range(augcount)], [label for in range(aug_count)] # number of samples to be dublicated and augmented in different ways data_in,labels_in = aug.cut_size_hard(data_in,labels_in) # Randomize region/aspectratio of samples data,labels = aug.augment(data_in,labels_in) # augment with init parameters
Data Chaser is a library that autocompletes empty fields in a csv file. Data Chaser uses Artifitial Intelignece based on multiple regressions, updating the values based un the uncertanty of the regressions to get the new updated values and ranges.In order to fill the empty fields we noticed that regressions have uncertainty, and base on the uncertainty of differents regressions of the different categories of the csv we made a neural network to get the information Name: DataChaser Format: .py Autors:
Manta is a PyTorch based neural network training library. Manta is powerful and flexible which lets you only write the code you need to. Sample Usage model = Model() train_loader, valid_loader = get_loaders() trainer = ModelTrainer(model, train_loader, valid_loader) trainer.fit(epochs=10) Manta also helps you monitor your training from anywhere. You can our the web interface to keep track of your experiments and visualize the progress.
A module that makes training models much easier. class ModelTrainer(): def init(self, model, train_loader, valid_loader=None, metrics=None, lr=10e-3, optimizer=None, loss_fn=None, save_path="model.bin", reporting=False):
Common sense modules that make building models easier. class GlobalAvgPooling(nn.Module): def forward(self, x): pass class GlobalMaxPooling(nn.Module): def forward(self, x): pass class Upscale(nn.Module): def init(self, factor=2): pass def forward(self, x): pass
fairscale is a PyTorch extension library for high performance and large scale training. fairscale supports:
Run a 4-layer model on 2 GPUs. The first two layers run on cuda:0 and the next two layers run on cuda:1. import torch import fairscale model = torch.nn.Sequential(a, b, c, d) model = fairscale.nn.Pipe(model, balance=[2, 2], devices=[0, 1], chunks=8)
Statistical-model-implementer
A library (if I do push it to pypi) that takes in training and test datasets and then applies statistical models, calculates metrics and also gives the best performing model.
Example: Using the Winconsin Cancer data that I had analysed earlier.
The train and test set are used as inputs for running the Implementer.
Models Used:
Logistic Regression
Decision Tree Classifier
Support Vector Classifier
K Neighbors Classifier
Random Forest Classifier
Adaboost Classifier
Metrics used:
Classification Report
Accuracy Score
Confusion Matrix
All other metrics that take y_test and predicted Y value as input.
To Implement:
KNN algorithm: need a way to find the optimal K value and then use that as the k_neighbors value
Add a way for the user to add the random state and other input parameters for the Statistical models
Keep on adding to this list.
PyHessian is a pytorch library for Hessian based analysis of neural network models. The library enables computing the following metrics:
The torchtrainers library is a small library for helping train
DL models in PyTorch. It helps with setting up training and optimizers,
keeping track of losses and metrics, and running learning rate
schedules.
See the accompanying notebook for an example of how to use the library.
BEMCheckBox is an open source library making it easy to create beautiful, highly customizable, animated checkboxes for iOS.
Learn more about the BEMCheckBox project, licensing, support etc.
Watermill is a Go library for working efficiently with message streams. It is intended for building event driven applications, enabling event sourcing, RPC over messages, sagas and basically whatever else comes to your mind. You can use conventional pub/sub implementations like Kafka or RabbitMQ, but also HTTP or MySQL binlog if that fits your use case.
Jelly is a library for animated, non-interactive & interactive viewcontroller transitions and presentations with the focus on a simple and yet flexible API. float: left"> float: left"> float: left"> float: left"> float: left"> float: left; margin-right: 32px"> float: left"> float: left">
With a few lines of source code, interactive UIViewController transitions and custom resizable UIViewController presentations can be created, without the use of the cumbersome UIKit Transitioning API. var slidePresentation = SlidePresentation(direction: .left) let animator = Animator(presentation: slidePresentation) animator.prepare(viewController: viewController) present(viewController, animated: true, completion: nil)
Presentation ObjectAnimator with the Presentationprepare FunctionUIViewController presentation function. class ViewController : UIViewController { var animator: Jelly.Animator? override func viewDidLoad() { super.viewDidLoad() let viewController = YourViewController() let presentation = SlidePresentation(direction: .left) animator = Animator(presentation:presentation) animator?.prepare(presentedViewController: viewController) present(viewController, animated: true, completion: nil) } } DO NOT FORGET TO KEEP A STRONG REFERENCE Because the transitioningDelegate of a UIViewController is weak, you need to hold a strong reference to the Animator inside the UIViewController you are presenting from or the central object that maintains your presentations. Interactive transitions can be activated for the slide and the cover transitions. If the transitions are to be interactive, only an InteractionConfiguration object has to be passed to the presentation. float: left"> float: left"> float: left"> float: left; margin-right: 32px"> Here 3 parameters play an important role. First, the completionThreshold, which determines the percentage of the animation that is automatically completed as soon as the user finishes the interaction. The second parameter is the actual type of interaction. Jelly offers the .edge and the .canvas type. In an .edge transition, the user must execute the gesture from the edge of the screen. When using the .canvas type, gesture recognizers are configured so that direct interaction with the presenting and presented view leads to the transition. The last parameter is called mode. Using the mode you can limit the interaction to presentation or dismiss interaction (default = [.present,.dismiss]). let viewController = YourViewController() let interaction = InteractionConfiguration(presentingViewController: self, completionThreshold: 0.5, dragMode: .edge, mode: .dismiss) let presentation = SlidePresentation(direction: .right, interactionConfiguration: interaction) let animator = Animator(presentation: presentation) animator.prepare(presentedViewController: viewController) Jelly 2.0 also provides a new feature called LIVE UPDATE.go-funk is a modern Go library based on reflect. Generic helpers rely on reflect, be careful this code runs exclusively on runtime so you must have a good test suite. These helpers have started as an experiment to learn reflect. It may look like lodash in some aspects but it will have its own roadmap. lodash is an awesome library with a lot of work behind it, all features included in go-funk come from internal use cases. You can also find typesafe implementation in the godoc. Why this name? Long story, short answer because func is a reserved word in Go, I wanted something similar. Initially this project was named fn I don't need to explain why that was a bad idea for french speakers :) Let's funk!
Multi-vendor library to simplify Paramiko SSH connections to network devices
Netmiko supports a wide range of devices. These devices fall into three categories: Regularly tested means we try to run our full test suite against that set of devices prior to each Netmiko release. Limited testing means the config and show operation system tests passed against a test on that platform at one point in time so we are reasonably comfortable the driver should generally work. Experimental means that we reviewed the PR and the driver seems reasonable, but we don't have good data on whether the driver fully passes the unit tests or how reliably it works.
TransmogrifAI (pronounced trns-mgr-f) is an AutoML library written in Scala that runs on top of Apache Spark. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse. Through automation, it achieves accuracies close to hand-tuned models with almost 100x reduction in time.
CppSharp is a tool and set of libraries which facilitates the usage of native C/C++ code with the .NET ecosystem. It consumes C/C++ header and library files and generates the necessary glue code to surface the native API as a managed API. Such an API can be
Termbox is a library that provides minimalistic API which allows the programmer to write text-based user interfaces. It is based on a very simple abstraction. The main idea is viewing terminals as a table of fixed-size cells and input being a stream of structured messages. Would be fair to say that the model is inspired by windows console API. The abstraction itself is not perfect and it may create problems in certain areas. The most sensitive ones are copy & pasting and wide characters (mostly Chinese, Japanese, Korean (CJK) characters). When it comes to copy & pasting, the notion of cells is not really compatible with the idea of text. And CJK runes often require more than one cell to display them nicely. Despite the mentioned flaws, using such a simple model brings benefits in a form of simplicity. And KISS principle is important. At this point one should realize, that CLI (command-line interfaces) aren't really a thing termbox is aimed at. But rather pseudo-graphical user interfaces.
Droppy is a self-hosted file storage server with a web interface and capabilities to edit files and view media directly in the browser. It is particularly well-suited to be run on low-end hardware like the Raspberry Pi.
Responsive, scalable HTML5 interface
Realtime updates of file system changes
Directory and Multi-File upload
Drag-and-Drop support
Clipboard support to create image/text files
Side-by-Side mode
Simple and fast Search
Shareable public download links
Zip download of directories
Powerful text editor with themes and broad language support
Image and video gallery with touch support
Audio player with seeking support
Fullscreen support for editor and gallery
Supports installing to the homescreen
Docker images available for x86-64, ARMv6, ARMv7 and ARMv8
General Information
Two directories will be used, one for configuration and one for the actual files: droppy maintains an in-memory representation of the files directory. If you're on slow storage and/or serving 100k or more files, the initial indexing on startup will likely take some time.
Gramm is a data visualization toolbox for Matlab that allows to produce publication-quality plots from grouped data easily and flexibly. Matlab can be used for complex data analysis using a high-level interface: it supports mixed-type tabular data via tables, provides statistical functions that accept these tables as arguments, and allows users to adopt a split-apply-combine approach (Wickham 2011) with rowfun(). However, the standard plotting functionality in Matlab is mostly low-level, allowing to create axes in figure windows and draw geometric primitives (lines, points, patches) or simple statistical visualizations (histograms, boxplots) from numerical array data. Producing complex plots from grouped data thus requires iterating over the various groups in order to make successive statistical computations and low-level draw calls, all the while handling axis and color generation in order to visually separate data by groups. The corresponding code is often long, not easily reusable, and makes exploring alternative plot designs tedious. Inspired by ggplot2 (Wickham 2009), the R implementation of "grammar of graphics" principles (Wilkinson 1999), gramm improves Matlab's plotting functionality, allowing to generate complex figures using high-level object-oriented code. Gramm has been used in several publications in the field of neuroscience, from human psychophysics (Morel et al. 2017), to electrophysiology (Morel et al. 2016; Ferrea et al. 2017), human functional imaging (Wan et al. 2017) and animal training (Berger et al. 2017).
Tested under Matlab 2014b+ versions. With pre-2014b versions, gramm forces 'painters', renderer to avoid some graphic bugs, which deactivates transparencies (use non-transparent geoms, for example stat_summary('geom','lines')). The statistics toolbox is required for some methods: stat_glm(), some stat_summary() methods, stat_density(). The curve fitting toolbox is required for stat_fit().
Habitat Lab is a modular high-level library for end-to-end development in embodied AI -- defining embodied AI tasks (e.g. navigation, instruction following, question answering), configuring embodied agents (physical form, sensors, capabilities), training these agents (via imitation or reinforcement learning, or no learning at all as in classical SLAM), and benchmarking their performance on the defined tasks using standard metrics. Habitat Lab currently uses Habitat-Sim as the core simulator, but is designed with a modular abstraction for the simulator backend to maintain compatibility over multiple simulators. For documentation refer here. We also have a dev slack channel, please follow this link to get added to the channel.
While there has been significant progress in the vision and language communities thanks to recent advances in deep representations, we believe there is a growing disconnect between internet AI and embodied AI. The focus of the former is pattern recognition in images, videos, and text on datasets typically curated from the internet. The focus of the latter is to enable action by an embodied agent in an environment (e.g. a robot). This brings to the forefront issues of active perception, long-term planning, learning from interaction, and holding a dialog grounded in an environment. To this end, we aim to standardize the entire software stack for training embodied agents scanning the world and creating highly photorealistic 3D assets, developing the next generation of highly efficient and parallelizable simulators, specifying embodied AI tasks that enable us to benchmark scientific progress, and releasing modular high-level libraries to train and deploy embodied agents.
If you use the Habitat platform in your research, please cite the following paper: @inproceedings{habitat19iccv, title = {Habitat: {A} {P}latform for {E}mbodied {AI} {R}esearch}, author = {Manolis Savva and Abhishek Kadian and Oleksandr Maksymets and Yili Zhao and Erik Wijmans and Bhavana Jain and Julian Straub and Jia Liu and Vladlen Koltun and Jitendra Malik and Devi Parikh and Dhruv Batra}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, year = {2019} }
phpMyFAQ is a multilingual, completely database-driven FAQ-system. It supports various databases to store all data, PHP 7.3+ is needed in order to access this data. phpMyFAQ also offers a multi-language Content Management System with a WYSIWYG editor and an Image Manager, real time search support with Elasticsearch, flexible multi-user support with user and group based permissions on categories and records, a wiki-like revision feature, a news system, user-tracking, 40+ supported languages, enhanced automatic content negotiation, HTML5/CSS3 based responsive templates, PDF-support, a backup and restore system, a dynamic sitemap, related FAQs, tagging, enhanced SEO features, built-in spam protection systems, OpenLDAP and Microsoft Active Directory support, and an easy to use installation and update script.
phpMyFAQ is only supported on PHP 7.3 and up, you need a database as well. Supported databases are MySQL, MariaDB, Percona Server, PostgreSQL, Microsoft SQL Server and SQLite3. If you want to use Elasticsearch as main search engine, you need Elasticsearch 5.x or later as well.
Kubernator is an alternative Kubernetes UI. In contrast to high-level Kubernetes Dashboard, it provides low-level control and clean view on all objects in a cluster with the ability to create new ones, edit and resolve conflicts. As an entirely client-side app (like kubectl), it doesn't require any backend except Kubernetes API server itself, and also respects cluster's access control.
Catalog
Navigation Tree
Extensive Caching
Multiple API Versions
Tabs
Copying Objects
Actions Bar
Keyboard Shortcuts
Most frequent actions have associated keyboard shortcuts.
Diff Editor
RBAC Viewer
Controls
Graph
Notifications
PlatyPS provides a way to:
Write PowerShell External Help in Markdown
Generate markdown help (example) for your existing modules
Keep markdown help up-to-date with your code Markdown help docs can be generated from old external help files (also known as MAML-xml help), the command objects (reflection), or both. PlatyPS can also generate cab files for Update-Help.
Why?
Traditionally PowerShell external help files have been authored by hand or using complex tool chains and rendered as MAML XML for use as console help. MAML is cumbersome to edit by hand, and common tools and editors don't support it for complex scenarios like they do with Markdown. PlatyPS is provided as a solution for allow documenting PowerShell help in any editor or tool that supports Markdown. An additional challenge PlatyPS tackles, is to handle PowerShell documentation for complex scenarios (e.g. very large, closed source, and/or C#/binary modules) where it may be desirable to have documentation abstracted away from the codebase. PlatyPS does not need source access to generate documentation. Markdown is designed to be human-readable, without rendering. This makes writing and editing easy and efficient. Many editors support it (Visual Studio Code, Sublime Text, etc), and many tools and collaboration platforms (GitHub, Visual Studio Online) render the Markdown nicely.
Common setups
There are 2 common setups that are used:
Use markdown as the source of truth and remove other types of help.
Keep comment based help as the source of truth and periodically generate markdown for web-site publishing. They both have advantages and use-cases, you should decide what's right for you. There is slight preference toward number 1 (markdown as the source).
Quick start
Install platyPS module from the PowerShell Gallery:
GocqlX makes working with Scylla easy and less error-prone. Its inspired by Sqlx, a tool for working with SQL databases, but it goes beyond what Sqlx provides.
Features
Binding query parameters from struct fields, map, or both
Scanning query results into structs based on field names
Convenient functions for common tasks such as loading a single row into a struct or all rows into a slice (list) of structs
Making any struct a UDT without implementing marshalling functions
GocqlX is fast. Its performance is comparable to raw driver. You can find some benchmarks here. Subpackages provide additional functionality:
CQL query builder (package qb)
CRUD operations based on table model (package table)
Database migrations (package migrate)
AJAX Micro Mini Library is a simplest library [ 2.8KB just ] to send data using post or get to an external fetch and send back the output ! Very user friendly and easy ! This pack comes with example PHP project to test
Simple Beat Saber Mod Installer
ModAssistant is a simple PC mod installer for Beat Saber, using mods from BeatMods. Beat Saber is the popular VR rhythm game where players must slash the beats as they fly towards them to the beat of lively music. ModAssistant comes with a number of great features like dependency resolution, mod uninstallation, and a complex theming engine. It features light, dark, BSMG and light pink themes, and can even have your own custom theme! Have more fun playing Beat Saber by customizing your experience with ModAssistant!
In the field of Lightweight Library learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.
For now, there are tremendous work opportunities for various IT fields. Most of the courses in Lightweight Library is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.
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