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this course contains the source code of the examples and code demos included in some of the "PostgreSQL and Java" trainings that [8Kdata][1] delivers. Java source code Included in the java directory are some maven-ized Java projects:
helloJDBC: an iterative approach to JDBC, where a simple example is improved across several executable programs, adding better JDBC constructs and Java best-practiceshellojOOQ: a simple project to show how to take back control of your SQL with [jOOQ][2]helloMyBatis: a simple project to show how to use the [MyBatis][3] mapperhelloProcessBuilder: connect to PostgreSQL via the stdinhelloPool: a simple project to show how to use of connection pool with [HikariCP][5] and [FlexyiPool][6]helloPLJava: call Java from inside of PostgreSQL via PL/Java Example database Included in the db directory is an example database used by the projects in the java folder. This database is derived from the [PgFoundry Sample Databases][4] world database.Training resources for LFCS certification (Linux Foundation Certified System Administrator)
Command-line Filesystem & storage Local system administration Local security Shell scripting Software management
Small program for the Raspberry Pi with Sense Hat: Displays IP address on the Sense Hat, useful for headless operation esp. for events like training workshops
The intended usage is as a program that can be included during startup of a Raspberry Pi with Sense Hat attached, enabling the Pi to announce its' IP address so that a remote system can use the IP address to connect to it (using ssh etc). The program logic relies on the target address in external_IP_and_port being routable, so if you're running this on a network not connected to the Internet or lacking a default route you'll need to alter external_IP_and_port to use an address that the Pi can reach on your network. It should be helpful for workshops using Raspberry Pis with Sense Hats allowing the Pis to be used 'headless' via ssh - which is what I wrote this utility for.
A theano warpper for implementing, training and analysing neural nets with convenience.
By using pre-defined layers and overiding the "+" operator, we can build deep networks within one line. Now building deep networks is as easy as stacking bricks together.
Easy to rearrange diffrent layers, be it pretrained or not, into new models. It allows you to build a network with different kind of layers, or reuse trained layers from some other models. It also brings convenience to form ensumbles. We make training methods completely separated to the models. With this separability, you can apply any training methods on any networks you built.
We also ensure that diffent kinds of training mechanisms get independent to each other. This allows you to combine different tricks together to form very complicated training procedures, like combining Dropout, Feedback Alignment, and unsupervised pretraining together, without having to define a new training class. A set of analyzing and visualizing methods are built into the definition of the models. So you are going to have a lot of analysing methods at hand right after your model is created. Most analysing methods allow interactive data updating, you can see how the weights/activations are changing during training epoches.
This course contains all the functions needed for the MLOT algorithm. this algorithm is useful for identification of low SNR particles in 3D. This README file contains a general overview of the algorithm, as well as installation instructions.
MLOT uses a linear logistic regression in order to create a general linear model of data based on a small sample dataset. The user is presented with this small training dataset and asked to identify all in focus particles of interest. With pixel locations of known particles, this linear model is generated and stored. The linear model is applied to unkown data in order to calculate the probability of particlse presence in the dataset. Using a threshold probablility value, unknown data is analyzed. Once all data has been analyzed and particle locations through time are known, these (x,y,z,t) points are then stitched together using a Hungarian simple tracking algorithm with gap detection. You can read more about the fundamental mathematics behind this algorithm, as well as preliminary results and error quantification, on out AIMES Biophysics paper (link coming soon).
MLOT has three major steps:
Preprocessing is a very imporatant step as it de-noises the images and increases the SNR so that the algorithm can better identify particles. This routines (currently) consists of two denoising steps.
This step presents the user with a total of ten z-slices and asks the user to select ONLY in-focus particles. This step is crucial because this determines the selection sensativity of the tracking algorithm. By selecting only in focus particles helps the program intrinsically reduce false positives. For more information on the GUI aspect of the training routine, see the 'Running the Code' Section of this README document. Once all in focus particles are selected through the 10 z-slices, a linear logistic regression is used to generate a linear model of the data and answer key provided by the user (where the particles are located). This linear model is used to track other datasets.
Tracking is done in two stages:
A python script for converting videos to series of frames for NN training Run by typing python video2image.py path -o output_dir --skip n --mirror The path leads to a video or a directory with only videos. The optional output folder specifies the directory where to save the images. Can be called with -o or --output. The optional skip parameter specifies every nth frame that should be saved. When running with the optional --mirror parameter, every second image saved is flipped.
Intention
ABC-List is a learning software which allowed the user to train his ability
structured thinking about a choosen topic and deepen his knowlegde in the
topics through key terms.
ABC-List is a [JavaFX] & [Maven] application, written in [NetBeans IDE].
Image: 0.2.0-PRERELEASE
Content
A simple program allowing you to practice password memory. It is not a password manager, passwords are securely stored as hashed strings hashed using bcrypt algorithm. If you like to rely on your memory and like to remember very long, random passwords full of strange characters, this program is for you. This script helps to build a good habit to practice your memory once every few days.
GUMA a Free Software Educational Program for elementary school students. What is GUMA? Guma is a ree Software Educational Program for elementary school students, that helps thet to practice in basic arithmetic operations of multiplication,adding, substraction and division. So how does help them? Is helps them by challenging them to solve random arithmetic praxis with random numbers. Also you can selext the number of the random of arithmetic praxis that you want to slove, the maximum value of a number that you want to participate in arithmetic praxis, and the type of arithmetic praxis that you want to practice. You can select to simulate the arithmetic operation.
What is PraxManager ? PraxManager is an online instrument destined to monitor and evaluate the practical training of the students in several specializations from healthcare education field, offering in the same time an overview on the evolution of the students during their training and the performance of the schools in time. PraxManager, the software developed in the project is tested and documented during the project lifetime, in local context, in the daily activities of other partner schools and in transnational mobilities for students.
The project entitled CoPE (Communities of practice in education) aims at creating an on line instrument destined to monitor and evaluate the practical training of the students in several specializations from healthcare education field, offering in the same time an overview on the evolution of the students during their training and the performance of the schools in time. PRAX-Manager, the software developed in the project will be developed, tested and documented during the project lifetime, in local context, in the daily activities of other partner schools and in transnational mobilities for students.
Krautli is supposed to become an (offline/online)app that allows users to log positions of their favorite plants and find them back at times certain of their plantparts become harvestable.
It started with pen&paper and advanced to a collection of notes and photos taken with a smartphone.
The app -is- will be capable of logging Positions and Access Data offline and synchronize with the server if needed.
Provide the means of ownership of one's data and trained models used in machine learning applications.
Store data as datapoints and add labels to each datapoint. Datapoints can be organized into different datasets and downloaded as labelled data.
Each datapoint derives from an entity. Entity is a special type which stores the data in a specific way.
When storing image data, this entity extracts the pixel array and stores it in 3-tuples of RGB components.
An open-source tool for ACM ACPC trainings to compare judge output with generated output
In order to check your program if it generates the right output, you can use diffcheker to compare them with the judge output.
diffchecker will display "Accepted" in green if the gen.out and judge.out are identical, "Wrong Answer" in red, or other problems in blue. In case of "Wrong Answer", it will display the cases that you should reconsider. If there are multiple ones, it will write them in a file called diff.txt.
sudo gem install colorize Horseshoe Hell is a mobile app designed for the 24 Hours of Horseshoe Hell climbing competition at Horseshoe Canyon Ranch Arkansas. This app is used for score keeping during the competition and can be used to view others scores, past scores, and can be used for training. The server side code and website to display results are included in this repository as well. This project (including the iOS and Android apps as well as the website and service side code) is owned by Luke Stufflebeam. Please consult the included license for details on usage retrictions. This software is open source and can be modified by anyone who wants to contribute to the project. Please contact Luke before attempting to modify the code. Luke Stufflebeam lucas@lucasstufflebeam.com
This tool is used for enhancing the process of collecting training samples from image datasets. cropping multiple regions. The tool allows you to use mouse-drag controls to crop ROIs and automatically store their image coordinates to a simple XML file for the validation of Ground Truth information once a statistical a statistical learning method or classifier has been trained.
Flash OS images to SD cards & USB drives, safely and easily. Etcher is a powerful OS image flasher built with web technologies to ensure flashing an SDCard or USB drive is a pleasant and safe experience. It protects you from accidentally writing to your hard-drives, ensures every byte of data was written correctly and much more. It can also flash directly Raspberry Pi devices that support the usbboot protocol
Supported Operating Systems
Note that Etcher will run on any platform officially supported by
Gson is a Java library that can be used to convert Java Objects into their JSON representation. It can also be used to convert a JSON string to an equivalent Java object. Gson can work with arbitrary Java objects including pre-existing objects that you do not have source-code of. There are a few open-source projects that can convert Java objects to JSON. However, most of them require that you place Java annotations in your classes; something that you can not do if you do not have access to the source-code. Most also do not fully support the use of Java Generics. Gson considers both of these as very important design goals.
toJson() and fromJson() methods to convert Java objects to JSON and vice-versa(pronounced "Zigh") A modern editor with a backend written in Rust. Note: This repo contains only the editor core, which is not usable on its own. For editors based on it, check out the list in Frontends. The xi-editor project is an attempt to build a high quality text editor,
The V Programming Language
Despite being at an early development stage, the V language is relatively stable and has
backwards compatibility guarantee, meaning that the code you write today is guaranteed
to work a month, a year, or five years from now.
There still may be minor syntax changes before the 1.0 release, but they will be handled
automatically via vfmt, as has been done in the past.
The V core APIs (primarily the os module) will still have minor changes until
they are stabilized in 2020. Of course the APIs will grow after that, but without breaking
existing code.
Unlike many other languages, V is not going to be always changing, with new features
being introduced and old features modified. It is always going to be a small and simple
language, very similar to the way it is right now.
The core module is the fundamental module that you need in order to use this library. The others are extensions to core.
The core module contains everything you need to get started with the library. It contains all
core and normal-use functionality.
dependencies {
implementation 'com.afollestad.material-dialogs:core:3.3.0'
}
The input module contains extensions to the core module, such as a text input dialog.
dependencies {
implementation 'com.afollestad.material-dialogs:input:3.3.0'
}
The files module contains extensions to the core module, such as a file and folder chooser.
dependencies {
implementation 'com.afollestad.material-dialogs:files:3.3.0'
}
The color module contains extensions to the core module, such as a color chooser.
dependencies {
implementation 'com.afollestad.material-dialogs:color:3.3.0'
}
The datetime module contains extensions to make date, time, and date-time picker dialogs.
dependencies {
implementation 'com.afollestad.material-dialogs:datetime:3.3.0'
}
The bottomsheets module contains extensions to turn modal dialogs into bottom sheets, among
other functionality like showing a grid of items. Be sure to checkout the sample project for this,
too!
dependencies {
implementation 'com.afollestad.material-dialogs:bottomsheets:3.3.0'
}
The lifecycle module contains extensions to make dialogs work with AndroidX lifecycles.
dependencies {
implementation 'com.afollestad.material-dialogs:lifecycle:3.3.0'
}
Picasso is a powerful image downloading and caching library for Android. Picasso allows for hassle-free image loading in your application—often in one line of code!
Many common pitfalls of image loading on Android are handled automatically by Picasso:
ImageView recycling and download cancelation in an adapter.SnapKit is a DSL to make Auto Layout easy on both iOS and OS X.
pjax is a jQuery plugin that uses ajax and pushState to deliver a fast browsing experience with real permalinks, page titles, and a working back button. pjax works by fetching HTML from your server via ajax and replacing the content of a container element on your page with the loaded HTML. It then updates the current URL in the browser using pushState. This results in faster page navigation for two reasons:
Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.
ExoPlayer is an application level media player for Android. It provides an alternative to Androids MediaPlayer API for playing audio and video both locally and over the Internet. ExoPlayer supports features not currently supported by Androids MediaPlayer API, including DASH and SmoothStreaming adaptive playbacks. Unlike the MediaPlayer API, ExoPlayer is easy to customize and extend, and can be updated through Play Store application updates.
Libra Core implements a decentralized, programmable database which provides a financial infrastructure that can empower billions of people.
In the field of Education Program 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 Education Program 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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