Programming X Courses Online

Live Instructor Led Online Training Programming X courses is delivered using an interactive remote desktop! .

During the course each participant will be able to perform Programming X exercises on their remote desktop provided by Qwikcourse.


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Programming X Training


Karrigell

About

Karrigell is an open Source Python web framework written in Python The Python 2 version is the stable release. A version for Python 3.2 and above was released in February 2011 Explains "how to" build web applications. This section only applies to the Python3 version This tutorial explains how to build a simple web based application : the example is a CD collection. The home page will show the list of records, with a counter of visits and a "login" link. People who successfully log in will be able to add / edit / remove records The first step is to install Karrigell. Download the latest version, unzip it in a folder, open a console window and in this folder run python Karrigell.py. This will start the built-in web server on port 80

7 hours

1,656 €

Lisp Programming

About

Lisp is a programming language. It is named after the collapsed phrase List Processing. If you have programmed before and would like to see a little bit of how Lisp works and is different from other programming languages, you can get an overview. Because Lisp itself is, technically, just seven operators, to become a useful language, much more needs to be implemented atop it. Common Lisp and Scheme are two such designs to create a useful programming language. Common Lisp is an ANSI standard, and features an extensive array of library functions. It is the more widely used of the two. Scheme is designed in a minimalistic fashion, with a very small amount of built in functions. This is probably true, but Scheme lacks many of the time-saving built-in functions of Common Lisp. Emacs Lisp is an implementation of Lisp in Emacs.

7 hours

1,656 €

mIRC Scripting

About

mIRC Scripting is a built-in, interpreted scripting language for the mIRC IRC client for Windows. It is an easy-to-use and flexible script for uses ranging from automating and simplifying IRC tasks to making mini-programs such as file servers and away systems. Some people even utilize scripts to completely skin and program the IRC interface. These are usually called "full scripts," while individual, smaller scripting projects are known as "remotes" or "addons," or "snippets" for really small scripts. This course will attempt to explain all the aspects of mIRC Scripting, starting from the very basic parts of the language for beginners, and delving into the more advanced topic for experts. If you're looking for something specific in this course, dive into the table of contents and find what you're looking for. 1. Starting From Scratch 2. Learning the Ropes

7 hours

1,656 €

Objective-C Programming

About

Objective-C is an object-oriented programming language. It was named after the concept of adding objects to the C language. If you have programmed before and would like to see a little bit of how Objective-C works and is different from other programming languages, you can get an overview. Objective C was introduced with NeXTSTEP and OPENSTEP, and was considerably extended in application due to its use with the Cocoa libraries under Mac OS X or the GNUstep libraries. However, you can program in Objective-C without these libraries if you wish. Because not everyone uses OPENSTEP or Mac OS X, we will introduce these library-specific details later. Familiarity with the C programming language is required, as Objective-C shares a lot with it, hence its name.

7 hours

1,656 €

Fundamentals of PBASIC Programming

About

This course is about PBASIC programming using the Parallax BasicStamp family of microcontrollers.

Content

  • Introduction
  • History
  • Hardware and Development Kits
  • PBASIC Editor

Computer Arithmetic

  • Data Types
  • Variables and Constants
  • Basic Arithmetic

Branching and Control Flow

  • Labels and GOTO
  • Branches
  • Loops
  • Subroutines

Port I/O

  • Input and Output
  • Serial Communications
  • PULSIN and PULSOUT
  • RCTIME

Advanced Arithmetic

  • Trigonometry Functions
  • Other Functions

7 hours

1,656 €

Programming Mac OS X with Cocoa for Beginners

About

This course aims to provide beginners with an introduction to the programming of Mac OS X Apps using Cocoa, and XCode, the free developer tools provided by Apple, Inc. Examples of Mac apps are: iTunes, Safari, Mail, iCal, Address Book, Microsoft Word, Microsoft Excel, etc. Using Objective-C, Cocoa and XCode as taught in This course will start your education in how to develop Mac apps. Many of the skills that you learn in This course can be used to build apps for iOS but there are some differences between Mac OS X apps and iOS apps. This course focuses on Mac OS X apps. Some knowledge of another programming language, preferably Objective-C. The following link is a link to a wikibook that covers Objective C Programming. Apple has a Mac App Store where it can sell your Mac apps for you and they will pay you 70% of the proceeds. The Mac App Store is available for users of Mac OS X, 10.6 and later by choosing from the Apple menu "App Store...". In general, this text is written to be followed in order from start to finish except that if you have experience in C, C++, Java or Python, you are encouraged to look at the relevant section of the Appendix to orient you to developing for Mac OS X and Cocoa.

Content

  • Contents
  • What Is Cocoa?
  • Building a Simple Mac App
  • Building Blocks of Mac OS X Apps
  • Building More Complex Mac Apps
  • More About the Cocoa Framework
  • Model-View-Controller (MVC) Design Pattern
  • More Cocoa Classes
  • Other Application Types
  • Managing Source Code
  • The Wikidraw App

7 hours

1,656 €

R Programming

About

This course is designed to be a practical guide to the R programming language[1]. R is free software designed for statistical computing. There is already great documentation for the standard R packages on the Comprehensive R Archive Network (CRAN)[2] and many resources in specialized books, forums such as Stackoverflow[3] and personal blogs[4], but all of these resources are scattered and therefore difficult to find and to compare. The aim of This course is to be the place where anyone can share his or her knowledge and tricks on R. It is supposed to be organized by task but not by discipline[5]. We try to make a cross-disciplinary book, i.e. a book that can be used by all people applying statistics to some specific fields. Some rules : We assume that readers have a background in statistics. This course is not a book about statistics but a book about how to implement statistical methods using R. We try to use terms which are already defined on Wikipedia such that people can refer to the corresponding wikipedia page each time they have some doubts on a notion. We also assume that readers are familiar with computers and that they know how to use software with a command-line interface. There are some graphical user interfaces for R but we are not going to explain how to use them in this course. Beginners should have a look at the Sample session for a first session with R. They can also have a look at the Statistical Analysis: an Introduction using R book.

7 hours

1,656 €

Exploring SDL

About

SDL is a cross-platform application programming interface (API) that allows you to code graphics across multiple platforms. Much of its workings is done behind the scenes leaving you, the programmer, an easier interface to those internal workings. Cross-platform programming is achieved by dynamically checking which Operating System the user is running. This is done by the use of conditional macros: These macros check the existence of predefined variables stored within the OS's compiler libraries. Depending on which are defined, the corresponding code would be executed for that particular system. This method also prevents OS specific code from conflicting with each other. The reason for this separation is because Operating systems have different ways of displaying graphics. Even though the code is different in each OS, most perform similar tasks such as creating a window, rendering to the window, grabbing user input, etc.. SDL brings these tasks together on a unified interface to allow you to basically code, compile, and run your program on multiple platforms. A video surface is a block of video memory that holds the pixel data. Each pixel is represented by a memory location within the memory block the surface is stored. This block of memory usually is a simple array, sometimes called linear memory. The main properties of a surface are its width and height in pixels and also its pixel format, which is the amount of data set aside for each pixel. This pixel size determines how many colors are available to be displayed onto the surface.

7 hours

1,656 €

TI 83 Plus Assembly

About

This course teaches TI-83 Plus Assembly Programming, an advanced programming language for TI-83+ and 84+ calculators. The TI-83 and TI-84 use a ZiLOG Z80 microprocessor. (The TI-89 uses a 68000 family processor and the TI-Nspire uses an ARM9 processor[1]). The TI-Nspire features an emulator by Texas Instruments for the TI-84+ SE. It can be used for Z80 programming, but it does not support undocumented commands and is not recommended. More Z80 assembly: Other TI calculators:

7 hours

1,656 €

VisSim

About

VisSim is a visual block diagram language for simulation of dynamical systems and model based design of embedded systems. It is developed by Visual Solutions of Westford, Massachusetts. VisSim is most often used to write embedded applications. Based on diagrams, the language has a different look and feel to traditional text-based programming languages based on the English language. For this reason, a VisSim manual comprises many pictures, rather than textual paragraphs. VisSim contains a large library of mathematical formulas, as well as built-in support for many popular chip technologies. VisSim is an extensible language, giving the user great flexibility to extend it, in the sense of adding routines to a library. It can also import ".m" files from MATLAB.

7 hours

1,656 €

Visual Basic .NET

About

Visual Basic .NET is a multi-paradigm, high-level programming language,from Microsoft that is suitable for most development needs. The language is designed with Rapid Application Development in mind, providing several tools to shorten development time. This course introduces Visual Basic .NET language fundamentals and covers a variety of the base class libraries (BCL) provided by the .NET Framework. GDI+ is a way to draw simple graphics and strings on a Form. 


14 hours

3,312 €

XRX

About

XRX or XForms/REST/XQuery is a simple and elegant web application architecture that leverages modern declarative and functional programming systems. XRX allows the developer to create rich-client web applications that perform complex functions without the need for middle-tier objects, relational databases or client-side JavaScript. XRX is based on three standards: These three standards have been created by the W3C standards organization and represent their vision of the future of web application development. For discussions on alternate definitions of the XRX web application architecture see What is XRX. This course is intended as an example that specifically uses all three of these technologies to create small applications that work together. There are two sibling Wikibooks that This course is designed to complement. The XForms Tutorial and Cookbook Wikibook has over 90 sample XForms application to help you become familiar with the XForms model and XForms controls. Although XForms has only 21 elements they can be combined in many different ways to build very complex web clients.

7 hours

1,656 €

XSLTForms

About

Welcome to the XSLTForms! XSLTForms is an open source client-side implementation of XForms which is a World Wide Web Consortium recommendation for creating web forms and web applications. XSLTForms is developed by Alain Couthures (of agenceXML at Bordeaux, France). These pages are for people who are just getting started. These pages discuss other topics of interest to XSLTForms users XSLTForms is an XForms 1.1 implementation with some XForms 2.0 features, and a number of implementation-specific extensions to the language. The coverage of 1.1, the portions of 2.0 that have been implemented, and the implementation-specific extensions are discussed separately below. Information for contributors to the XSLTForms codebase or to this course.


7 hours

1,656 €

Annotations of The Art of Computer Programming Volume 1

About

The purpose of these annotations is to encourage and help people to read one of the most influential scientific books of the 20th century. The annotations in This course apply to Volume 1 of The Art of Computer Programming, by Don Knuth. Please keep annotations succinct, clear, helpful, and professional. Annotations are marked with the edition number, page number and section number. Related equations, theorems, or otherwise numbered items should also be marked. Use bold for the numbering, for example Annotations must be presented in the correct order. Defer to the third edition of the course in case of conflict. TAOCP is copyrighted. Any quotes must respect the rules of copyright and/or specific instructions from the publisher.

7 hours

1,656 €

Computer Programming Principles

About

Computer Programming is the process of writing, testing, troubleshooting, debugging and maintaining of a computer program. Good programming practices mix art, craft and engineering discipline. This course will teach you the basic principles of computer programming and good programming practices. What This course will not do is teach you to use a specific programming language. This course may also be useful as part of a course on computer theory, computer engineering, or software engineering, along side learning a programming language, or as part of an advanced programming course.

7 hours

1,656 €

GNU Data Language

About

GNU Data Language (GDL) is a free software project hosted at Source Forge (GDL - GNU Data Language). A free IDL (Interactive Data Language) compatible incremental compiler (i. e. runs IDL programs). IDL is a registered trademark of ITT Visual Information Solutions. Features Full syntax compatibility with IDL up to version 7.1 (for 8.0 and later see below). ALL IDL language elements up to IDL version 7.1 are supported, including: Supported IDL 8.0 language elements: The file input output system is fully implemented (Exception: For formatted I/O the C() sub-codes are not supported yet) netCDF files are fully supported. HDF files are partially supported.

7 hours

1,656 €

Q3Map2

About

Q3Map2 has now been integrated with the GtkRadiant Project. Windows, Mac and Linux binaries for both 32-bit and 64-bit systems can be download from the project page. The Q3Map2 source code is now available through the GtkRadiant GitHub Repository. Official Support Forum @Splashdamage Current stable version is 2.5.17 Q3Map2 is a BSP compiler for games based on the id Tech 3 engine. It compiles .map files, which are editable with an editor, into .bsp files, which are binary files for the game and are not editable. It currently supports the following platforms: Q3Map2 was designed to replace the Q3Map.exe that comes with QERadiant, GtkRadiant and GMAX Tempest. However, there are significant enhancements that require a little twiddling to use, such as faster lighting and enhanced surface production. Fun Facts: Q3Map2 is a command-line utility. In general, users make use of Q3Map2 in one of three ways:

7 hours

1,656 €

Scribunto: An Introduction

About

Scribunto: An Introduction is a course for people who want to learn how to program using Scribunto. Scribunto enables users to embed the Lua programming language into wikis that use MediaWiki, the software that powers Wikipedia. This course covers how to get started, basic programming techniques, and how to use some of the Lua libraries that are unique to Scribunto. It is aimed at beginners to programming, particularly those who have some familiarity with the MediaWiki software, but may also be useful to experienced programmers who are new to Scribunto.

7 hours

1,656 €

BrainJam Tricky Mathematical Brain Training Game

About

BrainJam Tricky Mathematical Brain Training Game

BrainJam's challenging and interactive Math Problems helps you to sharpen and train your brain. BrainJam offers different kind of Math puzzles for everyone which tests your accuracy, aptitude and quick-thinking skills. Start exercising your brain now! Don't let your brain jam!

Screenshots


7 hours

1,656 €

Python Neural Network Classification

About

Python-Neural-Network-Classification

Python Neural Network that classifies through the training database the type of flower (Iris). This code example is from a Python classifying neural network with 3 different outputs, which informs which type of (Iris) out of the three is correct, taking into account the database used for training. The database presents three types of (Iris) which can be (Iris setosa), (Iris virginica) or (Iris versicolor). As for the Neural network, it has 4 inputs, two hidden layers with 8 neurons each, an output layer with three neurons, normal weight initialization function, 'relu' and 'softmax' activation function, and uses as optimizer the ' Adam '. Curiosity: To improve the neural network one has to test several values that the 'Keras' library offers, so it is advisable to study cross-validation.


7 hours

1,656 €

RandomPartitioner

About

RandomPartitioner

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.


7 hours

1,656 €

SMDL

About

Submodular Batch Selection for Training Deep Neural Networks

IJCAI 2019 Mini-batch gradient descent based methods are the de facto algorithms for training neural network architectures today. We introduce a mini-batch selection strategy based on submodular function maximization. Our novel submodular formulation captures the informativeness of each sample and diversity of the whole subset. We design an efficient, greedy algorithm which can give high-quality solutions to this NP-hard combinatorial optimization problem. Our extensive experiments on standard datasets show that the deep models trained using the proposed batch selection strategy provide better generalization than Stochastic Gradient Descent as well as a popular baseline sampling strategy across different learning rates, batch sizes, and distance metrics.


7 hours

1,656 €

Msgraph Training Microsoftflow

About

Microsoft Graph Training Module - Create a Microsoft Graph JSON Batch Custom Connector for Microsoft Flow & Azure Logic Apps

This module will introduce you to working with the Microsoft Graph JSON Batching REST API to access data in Office 365. You will learn how to create and configure a custom connector for Flow, access the the Microsoft graph JSON Batch API, and use the custom connector in a Flow to create a Microsoft Team.

Lab - Create a Microsoft Graph JSON Batch Custom Connector for Microsoft Flow & Azure Logic Apps

In this lab you will leverage the Microsoft Graph JSON Batching REST API to create a Custom Connector and Flow application.

Contributors

Version history


7 hours

1,656 €

GhostContactBook

About

Android Essential Training

GhostContactBook App

GhostContactBook is android application we will develop along with our training session. We will start from stretch with blank app and cover following milestones to take it to the Play Store eventually.

  • Hello World App (App Structure)
  • Understanding Activity and its UI builder. Navigating to other Activity.
  • Putting Recycler View & showing local data to RecyclerView
  • Developing Network Layer with Retrofit2
  • Fetching data from Rest API
  • Saving data to local database using GreenDAO ORM
  • Showing live data to our RecylerView
  • Getting ready to publish
  • Publishing on Play Store
  • Party!! (Recap and Wrap up)

7 hours

1,656 €

Master List

About

Master list of PhysiCell Training Modules

A list of modules in development for PhysiCell training Add a DONE after each once created

  1. What you need
  2. What is PhysiCell?
    1. Complex systems biology
    2. PhysiCell : microenvironment + cells
    3. Chemical microenvironment (via BioFVM)
      1. Reaction-diffusion
      2. cell sources / sinks
    4. PhysiCell agents
      1. State
      2. Phenotype
      3. Custom data
      4. Functions
    5. Training overview
  3. Introduction to agent-based modeling (optional / general)
    1. What is an agent?
    2. Off lattice vs on lattice
    3. Cell states
  4. Introduction to diffusion in biology (optional / general)
    1. Diffusion, decay, and length scales
    2. Neumann Conditions
    3. Dirichlet conditions
  5. PhysiCell

7 hours

1,656 €

Torchfit

About

TorchFit

TorchFit is a bare-bones, minimalistic training-helper for PyTorch that exposes an easy-to-use fit method in the style of fastai and Keras.
TorchFit is intended to be minimally-invasive with a tiny footprint and as little bloat as possible. It is well-suited to those that are new to training models in PyTorch.


7 hours

1,656 €

MemorizeIT

About

MemorizeIT

Project is created to increase memory and focus among children or adults who would like to take part in training. Rules of game are quite simple. You just need to count elements displayed on screen and remember their type. At the end you will be asked to write down your results and confront them with exact ones. This will be verified by marking your answer with proper color (correct - green, not - red). Difficulty of game can be increased by adding more types of elements to count, mixing their colors, turning sound off or changing time dedicated to each wave. It is also more difficult to play normal game mode (instead of static), because figures are moving which is additional distraction. I wish you enjoyable experience with game and best results in memory training. Good luck!

Beware:

Application was developed and tested on Fedora. Some functions may not work or behave in different way on other systems. Few adjustments were made for Windows too (if any problems with game speed in normal mode will occur please adjust method get_speed in game.py module to your personal needs).

Before using make sure that:

  1. Python3 is installed on your system (application was developed on version 3.6)
  2. All additional libraries are included:
  3. Necessary modules are in game directory:

7 hours

1,656 €

Training Skyline

About

WKND Sites Project

This is the code companion to a multi-part series on HelpX:

  1. Chapter 1 - Project Setup
  2. Chapter 2 - Pages and Templates
  3. Chapter 3 - Client-Side Libraries
  4. Chapter 4 - Style System
  5. Chapter 5 - Custom Component
  6. Chapter 6 - Unit Testing
  7. Chapter 7 - Header and Footer
  8. Chapter 8 - Landing Page

    Modules

    The main parts of the project are:

    • core: Java bundle containing all core functionality like OSGi services, listeners or schedulers, as well as component-related Java code such as servlets or request filters.
    • ui.apps: contains the /apps (and /etc) parts of the project, ie JS&CSS clientlibs, components, templates, runmode specific configs as well as Hobbes-tests
    • ui.content: contains sample content using the components from the ui.apps
    • ui.tests: Java bundle containing JUnit tests that are executed server-side. This bundle is not to be deployed onto production.
    • ui.launcher: contains glue code that deploys the ui.tests bundle (and dependent bundles) to the server and triggers the remote JUnit execution

7 hours

1,656 €

Msgraph Training Authentication

About

Microsoft Graph: Authenticate and Connect to Microsoft Graph

This module walks through authentication scenarios with Microsoft Graph.

Lab

In this lab, you will walk through authentication and permissions scenarios leveraging the Microsoft Graph using the Graph SDK and Microsoft Authentication Library (MSAL).


7 hours

1,656 €

Msgraph Training Smartui Components

About

Microsoft Graph Training Module - Smart UI with Microsoft Graph, Pickers and Cards

This module will show you how to build a web user interface with Office UI Fabric components and invoking Office 365 pickers for interacting with data from the Microsoft Graph.

Lab - Smart UI with Microsoft Graph, Pickers and Cards

In this lab, you will walk through building a web user interface with Office UI Fabric components and invoking Office 365 pickers for interacting with data from the Microsoft Graph.


7 hours

1,656 €

ThinkerFarmTrainer

About

ThinkerFarmTrainer

ThinkerFarmTrainer V1.0.0

Introduction

ThinkerFarmTrainer is a toolset for training ssd object detection models. Originally i made this toolset for myself aiming to ease my custom object detection model training process so i'm sharing here and hope you will find useful. I use transfer learning method on ssd mobilenet v2 quantized 300x300 coco. Model performance is quite good for variety of mobile and edge projects.

Features


7 hours

1,656 €

RT Programming

About

RT_programming

A tool for generating a range of styles of appropriate resistance training programs for strength. Edit 1RM, monthly volume, and set flags to the program you would like to have generated. Existing constants other than 1RM and volume are based on hypothetically correct values for squat programming described in the Joe Rogan Experience ep. 1399


7 hours

1,656 €

AuthID

About

AuthID

A Python3 program to identify the author of an unknown text. This is done by analysing the charactertistic ngram frequencies of the authors' works in the training set, and the matched to data in the test set.

Prerequisites

You need python3 and nltk installed. Further, there should be 2 directories - "Train Data" and "Test Data", present in the directory where the py file is located.


7 hours

1,656 €

Hyper Manager

About

hyper_manager

This UI tool is meant to assist with hyperparameter search when training neural networks. It invokes an external process to do the training, expecting that process to checkpoint its work and eventually exit. Different hyperparameters are tracked based on the command-line options to the training program required to set them up. The rate of training progress is evaluated based on Tensorboard log files emitted by the external process, and at each interval a hyperparameter set is randomly selected for additional training, weighted based on configurable parameters such as convergence rate or total training time. Hyperparameter sets may be selected for plotting of the loss curves, or interactively inhibited from additional training. Additional hyperparameters may be manually added at any time.

Dependencies

In addition to Tensorflow, this tool depends on PySide2 and matplotlib to function:

  • pip3 install matplotlib
  • pip3 install PySide2 If you have Tensorflow in a virtual env, all three packages must be in the same env, and the tool must be run from that environment.

    Instructions to use

    To begin, invoke File/New Session. You will be prompted for some properties of the session:

  • "Session name" is the name of the new session to create. A new folder will be created at the specified path to contain the session settings and training progress.
  • "Session path" is an existing folder to put the new session in.
  • "Training executable" is the tool to invoke to perform a training iteration.
  • "Training options" is a space-separated list of command-line options to be provided to the tool, common to all hyperparameters. This might be invariant things such as a dataset file location, configuration of a common metric to use, and training duration. NOTE: it is important that the training process exit on its own after a period of time (e.g. five minutes or so); probably, these options should configure that interval.
  • "Checkpoint subdir" is the location of the checkpoints (e.g. saved weights) emitted by the training process, relative to the working directory of the process. Every distinct hyperparameter set is invoked in its own subdirectory of the session folder, and this directory is expected to be found under that. When resuming training, the manager will look for json files in this folder, identify the most recent one lexicographically, and pass it to the training process with a '--resume-from' argument.
  • "Logs subdir" is the location of log data emitted by the training process, relative to the working directory of the process. Error stats and plots will be based on scraping the Tensorboard logs found here. If there is a single metric in the log, that will be used as the 'error'; with no metrics, the loss will be used. Multiple metrics are not currently supported. Once the session is created, sets of hyperparameters to test can be added via the Set menu, and the training session can be started from the Run menu. The TTZ column can be used to estimate which hyperparam sets will reach lower error values sooner. It is based on a linear fit to the error curve, and the time for that projected line to intersect zero ("Time To Zero"). Obviously, no training process follows a linear progression indefinitely, so this should not actually be treated as a meaningful unit of time, only as a relative ranking between sets taking into account current error and error rate. This column is also the default metric used to weight random selection when picking a set to train for a new interval. Individual hyperparam sets, or groups of sets, may be enabled and disabled manually by selecting them and right-clicking, or using the Sets menu. Additionally, a threshold on the variance can be configured to automatically disable sets if the validation error becomes substantially worse than the training error.

7 hours

1,656 €

Network Analysis

About

network_analysis

Tools for the analysis of networks, including trained networks, in Python 3.7. Training is done by pytorch. There are three main packages These tools are not currently prepared for use by a wider audience (in particular, documentation is sparse and inconsistent). Future updates may change this. There are four main modules: (1) models.py contains PyTorch torch.nn.Module objects. These are network models that can be used for training. (2) model_trainer.py contains a function train_model that takes in a model, optimizer, loss function, etc. and trains a model. (3) model_output_manager.py contains tools for recording the runs that have taken place. Any time a network is created and trained, the parameters used for this can be handed to model_output_manager in order to create a new row in a table that records every run. If you go to train a model with the same parameters as before, this utility can automatically load the previous trained model instead. ToDo: Show an example of how this works. (4) model_loader_utils.py contains utility functions for loading models over epochs. It can return the hidden unit activations, the weights, or the models themselves over epochs.


7 hours

1,656 €

Mysterio

About

mysterio

A platform for training, labeling, deploying and retraining image classification model.

Approach & Solution

Project Structure

main.py :
    embedding the machine learning model to flask
    routing functions
templates/ :
    frontend files
static/ :
    media files
model/ :
    pre-trained pickle file

Dependencies

Flask
PyTorch
Matplotlib

7 hours

1,656 €

Data Training For Machine Learning

About

This course was developed using the concept of Machine Learning and the programming language Python 3. Machine learning is the study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. The main focus of this course is to monitor how increasing the amount of data training for a machine learning algorithm can recognize handwritten digits that increases the accuracy of the algorithm.


7 hours

1,656 €

Deploying Azure Terraform

About

Deploying Resources in Azure using Terraform. Using Terraform, we will deploy the most used resources in Azure.

Login to Azure with Service Principal

az login --service-principal -u $client_id -p $client_secret -t $tenant_id

Resouces:

Resource Groups Network- Vnet, Subnets, NSGs, Public IPs etc VMs - Windows VM, Linux VM Storage Accounts Azure SQL Server and Database


7 hours

1,656 €

Boja

About

Boja

An end to end object detection tool. All the way from capturing and labeling a dataset of images, to training and deploying an object detection neural network. This package makes use of the Harvesters machine vision image acquisition library for capturing images so a GenICam compliant machine vision camera is required. Boja translates to "let's see" in Korean. 


7 hours

1,656 €

Jano

About

Jano

God of beginnins, time and trasitions...

What is Jano ?

Jano is a time slicer designed to train and test time correlated machine learning models. Jano operates by "walking" along pandas dataframes with at least one time variable. Users can think of Jano as an iteration over a dtaframe of sklearn.model_selection.TimeSeriesSplit where a few features are addes such as: definning training size iteration over time, test size, a definen gap of time between train and test, etc... Jano was essentially designed to test how will a defined model will behaive over time based on your disposable trasactional data. On the other hand tryes to tackle some of the following questions: How much data should be used in train and test to make robust predictions over time ?When the model should be re trained ?, How long will the model maintain performance ?, Do distribution attributes change over time ?, Does my target distirbution change over time ?

What is a mask ?

A mask is defined by the users and simply defines how would you like to iterate over a defined dataframe, check this example: import pandas as pd df = pd.DataFrame('date':['01-01-2020', '02-01-2020', '03-01-2020', '04-01-2020', '05-01-2020', '06-01-2020', '07-01-2020', '08-01-2020', '09-01-2020'], 'attrib':[9,4,2,3,4,5,6,1,2,4] 'target':[0,1,2,3,4,5,6,7,8,9]) import jano as jano jano = Jano(df)

Define a jano mask:

jano.mask(train_days = 8, gap = 1, test_days = 1, target = 'target', train_date_attrib = 'date') In this example Jano uses 8 days to train, tests with 1 day and leaves 1 day as a gap from the end of the train until the start of the test period. If you want to iterate over a dataframe with the defined mask then you want to "walk" over a dataframe, check te following example...


7 hours

1,656 €

Labs11 TrainingBot BE

About

Introduction

Training bot allows managers of teams to send notifications to their teammates on a predefined schedule.

What is Training Bot?

Training Bot is a learning application that lets a team leader create a series of trainings and deliver them at a scheduled time via text or email to assigned learners. The user will be able to add members and assign them to a scheduled set of trainings with a start date. Each training will have a title, text body, and link. They should be small snippets that fit well in a text message sized post.

Training Bot empowers team leaders with tools to assist with their teams continual learning.


7 hours

1,656 €

Musical Dynamics Training Software

About

Musical Dynamics Training Software for Digital Piano

Description

Simple python program that helps pianists practice and visualize their dynamic expression in real time.

Primary Use

While there are many programs that help learn to play piano or improve sight-reading, none have focused on practicing and fine-tuning dynamic expression. This program provides a very simple user-interface to visualize your dynmics:

  • Displays a colored bar extending above the piano key that denotes the velocity of each keypress
  • Lists the level at which you are playing (pp, p, mp, etc.)
  • Future feature to indicate how closely aligned simultaneous notes are played

    Credits

    Utilizes python-rtmidi to capture midi messages in realtime


7 hours

1,656 €

Perfect Pitch Training App

About

Perfect Pitch Training App

A simple app for practising absolute (perfect) pitch recognition. The app plays a series of random pitches sampled from multiple octaves and a variety of instruments. In each sequence the target note is played 3-5 times interspersed with the random pitches. The goal is to memorize the target pitch. After a short delay (1.7 seconds) a red message ('that was not the target pitch') or a green message ('that was the targe pitch'). This is intended to facilitate memorizing the sound of the pitch without using relative pitch.


7 hours

1,656 €

Boss.AI Training Tools

About

Boss.AI-Training-Tools

Boss.AI Training Tools is a set of tools that can be used to mod or train Boss As of 4/12/2020 the Boss.AI Training Toolkit has 1 tool. This tool is the conversation.bat / conversation.sh tool and it is useful if you want to suggest changes to Boss Chatbot or make a modification to the conversation based learning. It asks for what the user should say, then what Boss should say. It then outputs a fake chat log based off Discord's chat logging and that file can be placed in the AI training repo before training to add that conversation to training.


7 hours

1,656 €

Learn Quantization Apache MXNet (Incubating)

About

Quantization is one of the popular compression algorithms in deep learning now. More and more hardware and software support quantization, but as we know, it is troublesome that they usually adopt different strategies to quantize.

Here is a tool to help developers simulate quantization with various strategies(signed or unsigned, bits width, one-side distribution or not, etc). What's more, quantization aware train is also provided, which will help you recover the performance of quantized models, especially for compact ones like MobileNet.

Content

  • Simulate quantization
    • Usage
    • Results
  • Quantization Aware Training
    • Usage
    • Results
    • Deploy to third-party platform
      • ncnn

7 hours

1,656 €

Keras Cnn 1drnn Ctc

About

A toolkit for training neural networks to perform line-level Handwritten Text Recognition (HTR)

The toolkit is built on top of TensorFlow/Keras. It is shipped with a ready-to-train CNN-1DRNN-CTC [1] model and all the surrounding code enabling training, performance evaluation, and prediction. In a nutshell, you only have to tell the toolkit how to obtain the raw handwriting examples of a form line image -> text. The rest will be taken care of automatically including things like data preprocessing, normalization, generating batches of training data, training, etc. You can train the model on the IAM Handwriting dataset as well as your own. Also, the code should work for arbitrary written language, not just English (at least in theory).

Key features

Built-in models

Pre-requisites


7 hours

1,656 €

Sagemaker Mlflow Container

About

Introduction

AWS SageMaker compatible container to run mlflow trainings. The container is created with using [Amazon SageMaker Containers] library

Quickstart

Requirements: Create quickstart project dir $ mkdir sm-mlflow-quickstart && cd sm-mlflow-quickstart Clone mlflow repo to get sources of MLFlow project examples and set env var to one of examples $ git clone git@github.com:mlflow/mlflow.git MLFLOW_PROJECT_PATH=mlflow/examples/sklearn_elasticnet_wine


7 hours

1,656 €

Hiddenlayer

About

HiddenLayer

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.

1. Readable Graphs


7 hours

1,656 €

Imagenet Training

About

ImageNet Training

Pytorch code for training imagenet with fp16

  1. Install pytorch,torchvision
  2. Install apex conda install -c conda-forge nvidia-apex
  3. (optional) install data loading speedups: conda install -c thomasbrandon -c defaults -c conda-forge pillow-accel-avx2 conda install -c conda-forge libjpeg-turbo

7 hours

1,656 €

Learning Labs

About

FRINX Learning Labs

Get to know FRINX software and solutions hands on through a series of labs.

Labs

  • FRINX OpenDaylight & UniConfig - First Steps
  • A sample application to create LACP link bundles
  • Getting LLDP topology data
  • Obtain platform inventory data
  • Create Layer-2 VPN
  • Create access-lists
  • Create OSPF routing process
  • Create eBgp routing process
  • Create access-lists with Uniconfig Native
  • Create OSPF routing process with Uniconfig Native
  • Create eBgp routing process with Uniconfig Native

7 hours

1,656 €

Cliplayer

About

cliplayer

cliplayer helps to script shell based lectures or screencast trainings. The player takes a playbook with shell commands that are executed live like you write them at this moment.

Motivation

When holding lectures or recording screencast you often need to devide your attention between talking and typing at the same time. This cliplayer helps you to concentrate more on what you want to teach instead of what you need to type.

Corona Edition

It was planed to release the player along with an other project. Since many people are now forced to stay at home, maybe the cliplayer can help some people to make awesome bash tutorials.


7 hours

1,656 €

Cls2det

About

Introduction

cls2det is an object detection tool based on PyTorch. Unlike most popular object detection algorithms, cls2det implement object detection with only a classifier pre-trained on ImageNet dataset.

Benchmark

Evaluation on class "dog" on PASCAL VOC 2012 dataset:

Requirements


7 hours

1,656 €

BasicNeuralNetwork VectorTraining

About

Vector training of Basic Neural Network

Very simple example source code of how to train vectors in Brain.JS with Neural Network.

Preferences

You can find preferences below require statements at index.js.

Datas

Training

Others

Initialization

Before we begin, you need to know one fact that training will stop after completion of current train(one word) when you send SIGINT signal to console that means your data will be saved safety after done of active training node.

  1. Download model from online or local source and locate it to data/model.txt.
  2. Set loadFromPreviousModel to false and test this is running correctly.
  3. After running you'll get values from console, and set loadFromPreviousModel to true to save your data next time. As I said at preferences section, you'll lose all data if you run training with this option set to false.

7 hours

1,656 €

Face Recognition

About

Face-Recognition

A Minor Small project which trains the dataset of Images and afterward can be use for face detection of that person. I the project i used numpy, openCV and faceRecognition python packages. For front face training data i used the haarcascade_frontalface_default.xml file.

Prerequisites =

=> Numpy :- pip install numpy => OpenCV :- run pip install opencv-contrib-python => FaceRecognition :- pip install face-recognition

Command for Annaconda Prompt

=> Numpy :- conda install numpy => OpenCV :- conda install -c menpo opencv => FaceRecognition :- conda install -c akode face_recognition_models Note: Install opencv-contrib-python package instead of opencv-contrib as it contains the main modules and also contrib modules.


7 hours

1,656 €

Plasma Real Time

About

Real-Time Streaming Example Using Shared Memory (Plasma Store) and Scikit-learn

This code demonstrates how to efficiently distribute and process real-time messages using [the Apache Arrow Plasma][1] in-memory object store. We also show how we can incrementally train an online linear model using [the Scikit-learn's SGDRegressor][2]. The script creates an instance of Plasma store, starts up one producer and multiple consumer processes.

  • Producer process:
  • Consumer processes: The advantage of this approach is that the producer process is not affected by the count of consumer processes and their speed. It is important that the producer and consumer processes utilize the same logic to generate a series of consecutive message ids. Please note that the Plasma API is under active development and it is currently (as of 2020/05) not stable.

7 hours

1,656 €

Vikos

About

Vikos

Vikos is a library for supervised training of parameterized, regression, and classification models Design Goals

  • Model representations, cost functions, and optimization algorithms can be changed independently of each other.
  • Generics: Not committed to a particular data structure for inputs, targets, etc.
  • If the design goals above can only be achieved by sacrificing performance, so be it. Current State Just starting to get the traits right, by continuously trying new use cases and implementing the learning algorithms.

7 hours

1,656 €

Terracotta Bank

About

Terracotta Bank

Terracotta Bank is an intentionally-vulernable web application, useful for practicing detection, exploitation, and mitigation of common web application security vulnerabilities. Terrcotta Bank binds locally to port 8080 by default, and while it is running, the machine on which it is running is vulnerable in the same way that this application is.


7 hours

1,656 €

Hamstir Gym

About

OpenAI Gym environments for HAMSTIR Autonomous Mobile System for Testing Intelligent Robotics (HAMSTIR)

The goal of this project is to create a simple robot guided by a monocular camera that can be trained end-to-end in simulation using reinforcement learning. This project is in early development, so not everything works yet ;) We lean heavily on domain randomization, with three rooms with random texture walls. See a demo of a trained policy in one room (birds eye view on left, robot-eye view on right):

Dependencies

Compared to other robotic simulations intended for sim-to-real transfer, the dependencies are light:

  • OpenAI gym
  • pybullet >= 2.4.0
  • pyquaternion The pybullet environment makes use of texture and camera randomization to allow sim-to-real transfer. Whether this is successful is yet to be shown.

7 hours

1,656 €

AppFeedbackSystem

About

AppFeedbackSystem

AppFeedbackSystem provide your app with dialog with feedback options; Frequently Asked Questions, Feature Request, General Feedback, Bug Report, & Contact Us.

Screenshots

I have learned a lot in training in this library and I have stopped developing it since I learned what I need.


7 hours

1,656 €

Ai Lab

About

ai_lab

ai_lab is a library for loading datasets, data augmentation, training management, experiment management and result documentation

Sample

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


7 hours

1,656 €

GeneticAlgorithmSim

About

Abstract This learning tool provides users with an interactive and engaging way of understanding more about the nature of learning algorithms, specifically, the genetic algorithm. Users become more familiar and comfortable with something that may have previously been inaccessible or intimidating. The game consists of a set of cannons that represent the population in a genetic algorithm. Using sliders, correlating to specific variables of the algorithm and the landscape, the user is able to adjust and experiment to see real-time effects through a hands-on and highly visual learning experience. This active, real-time feedback reinforces learning and solidifies understanding of the genetic algorithm - its structure, limitations, and applications. Opening and Running code package instructions:

  1. Must download:

    1. Download the zip from github, unzip

    2. Open Epic Game Launcher, and click the LAUNCH Unreal Enginge in the top right-hand corner

    3. Select the "Protolith" file from the unzipped file package


7 hours

1,656 €

Openshift Vagrant Fundamentals

About

The OpenShift Vagrant project aims to make it easy to bring up a real OKD cluster by provisioning pre-configured Vagrantfile of several major releases of OKD on your local machine.

Content

  • Overview
  • Prerequisites
  • OKD Version Support
  • Getting Started
  • Install original cluster using Ansible
  • Open web console

 


7 hours

1,656 €

Neuronmancer

About

Neuronmancer is a C / CUDA program for creating, training, and evaluating feedforward artificial neural networks that learn to recognized handwritten digits using backpropagation. Training can be performed on the host machine or a CUDA-enabled GPU device.

Project Prerequisites

  • cuda-toolkit-8-0 or higher
  • GCC
  • GNU Make (or you can compile yourself using: nvcc main.cu -o neuronmancer)

7 hours

1,656 €

Pycoach

About

PyCoach: a training package for PyTorch

PyCoach is a Python package that provides: The main purpose of PyCoach is handle training, evaluation and prediction, leaving other tasks like create the network model and dataloaders to the users. This is an initial personal project and contribution to it is appreciated. Actual status: Working with PyTorch 0.4!

To Do:


7 hours

1,656 €

Neuroplasticity

About

Neuroplasticity

A neural network training algorithm implementing synaptic plasticity models. The weights of the network are updated using hebbian and homeostatic-inspired mechanisms.

Hebbian Mechanism

For hebbian learning, arcs are "strengthened" by updating weights in the direction that predicts increased reward. These update vectors change when the reward declines rapidly and depend on how much the weights influenced the outcome.

Homeostatic Mechanism

For homeostatic training, weights are scaled by a factor in an attempt to stabilize network activity to a dynamic setpoint. Network activity is computed as the arithmetic average of the average activation of each node. The setpoint is then computed as the running mean of this network activity which converges since the homeostatic weight updates reinforces the network activity to previously predicted values.


7 hours

1,656 €

Whatsappchat Cleaner

About

whatsappchat-cleaner

This set of tools are used in an attempt to re-create my own personality by utilizing Whatsapp Chat data as a training set. Run the following Python files in the following order:

  1. chatparser.py
  2. chatseparator.py
  3. mytagdelete.py
  4. otherstagdelete.py
  5. combine.py

7 hours

1,656 €

Learn Gcp

About

GCP in a nutbash shell

GCP in a bash shell is an educational repository to share study resources to learn [Google Cloud Platform (GCP)][1].

Goal

It started as a personal project to store notes while preparing the [Professional Cloud Architect][2] certification. I found valuable the ability to revisit previous notes to help understand intricate concepts and have labs artifacts (lab procedures, code samples) handy. I hope this project could help others to study and pass their GCP certification exams.

Project Status

Unedited notes available in [docs/cert-pca/][3] Unedited notes available in [docs/cert-ace/][6]

What is this project good for?

What is this project not good for?


7 hours

1,656 €

MS Graph Training Reactspa

About

Microsoft Graph Training Module - Build React single-page apps with Microsoft Graph

This module will introduce you to working with the Microsoft Graph in creating a React single-page application to access data in Office 365.

Lab - React single-page apps with the Microsoft Graph

In this lab you will create a React single-page application, configured with Azure Active Directory (Azure AD) for authentication & authorization, that accesses data in Office 365 using the Microsoft Graph.


7 hours

1,656 €

Image Processor

About

image-processor

An image processor written in C#. Applies math operators on a given image set. Results in a set of data parameters to use for neural network training. I developed this at university to transform greyscale image datasets of 19x19 pixels to a 7x7 image using a set of mathematical operators (Sobel, Kirsch, Prewitt, Scharr and Isotropic). The image is downscaled by moving a 3x3 operator along the given bitmap, multiplies against it, sums up the 9 values and returns that as a single paramter for a NN to train with. It then continues to move along the image until all pixels have been transformed.

Dataset Output

Each transformed face is represented as a single 50 column row in the output CSV file. The 50th column specifies the class (0 non-face, 1 face).

Features

  • gui interface
  • live preview window (cool effect)
  • multiple types of math operators
  • multi-threaded
  • displays image processing rate
  • shows a realtime progress bar
  • shows the time elapsed and total processing duration

    Screenshot


7 hours

1,656 €

FareStart

About

FareStart

These are programs to support development at FareStart.org - a Seattle organization for training people to work in the restaurant trades The repository is an extension of work I did for other organizations in the repository lordjoe/SpreadSheetTools - it is cloned to allow reomval of code irrelevant to the work for FareStart and to allow addition of APIs for tools specific to the restaurant Environment.


7 hours

1,656 €

Blob Customvision Trainer

About

Blob Storage to Custom Vision Uploader

Note: Early version, not fully tested. This script is designed to pull images directly from a blob storage account and upload training images to the Custom Vision Service in batches. This is beneficial if you would like to upload images directly from Azure Blob Storage to Custom Vision Service, without having them on a publicly accessible URL or downloading them to local storage. You can run this script from a local machine, from an Azure VM, or anywhere you can execute Python Code and have internet connectivity! Initial tests: Instructions: 1) Keys - Create a keys.json file in the same directory as the script. You can use the keys_sample.json file as a template. This local file will contain your Azure Storage key, Custom Vision training key, and Custom Vision project id (which can be found in the project settings page). { "storage_key":"", "customvision_projectid":"", "customvision_training_key":"" } 2) Training Data - The script expects data to be structured in the blob storage account into different directories, with each directory containing files with specific tag(s). Examples with cats and dogs classification:

3) Script Execution:


7 hours

1,656 €

Abyme

About

Abyme (Fractals)

Abyme is a tool for writing Deep and Sophisticated (Training) Loops. Training loops involve a lot cuisine:

  • When to save a model
  • What to print on screen
  • When?
  • What information capture for debugging
  • In what format save them?
  • At which periodicity? With Abyme training loops are written as fractals that go deeper and deeper, allowing the user to dynamically plug events at user-defined steps. Sounds complicated but it actually makes everyting much simpler. criterion = torch.nn.modules.loss.MSELoss() optimizer = torch.optim.Adagrad(model.parameters(), lr=0.01) epoch_looper = AB.IterrationLooper() train_data_looper = AB.DataLooper(get_data_loader(train=True, mask_targets=True, batch_size=500)) train_pass = AP.SupervisedPass(model, optimizer, criterion, update_parameters=True, inputs_targets_formater=data_formater) train_stats = AB.Stats(caller_field="last_loss") test_data_looper = AB.DataLooper(get_data_loader(train=False, mask_targets=True, batch_size=10000)) test_pass = AP.SupervisedPass(model, optimizer, criterion, update_parameters=False, inputs_targets_formater=data_formater) test_stats = AB.Stats(caller_field="last_loss") csv_result = AB.CSVWriter(filename="test2.csv") def handle_epoch_end(name, epoch_looper, data_looper, csv, save_model, stats_caller_focus): res = ( AB.NewLowTrigger("average").focus(stats_caller_focus)("dig", AB.Print(["==>New %s average low, epoch"%name, epoch_looper.get('counter'), "batch:", data_looper.get("counter")]), AB.If(condition=save_model)("dig", AP.SaveModel(model=model, filename=name, prefix=epoch_looper.get("counter")), ), AB.PrettyPrintStore(fields=["average", "std", "min", "max"], prefix="%s.new.low." % name), csv.add_caller_to_line(fields=["average", "std", "min", "max"], prefix="%s.new.low." % name), ), AB.MovingStats("average", window_size=100).focus(stats_caller_focus)("dig", AB.PeriodicTrigger(100, wait_periods=1)("dig", AB.PrettyPrintStore(fields=["average", "std", "min", "max"], prefix="%s.loss.moving." % name), csv.add_caller_to_line(fields=["average", "std", "min", "max"], prefix="%s.loss.moving." % name), ) ), ) return res

    AB.Ground()("dig", epoch_looper.setup(10)("start", AB.Print(["Training starts"]) ).at("iteration_start", csv_result.open_line(), train_data_looper("iteration_end", train_pass("end", train_stats, ) ).at("end", test_data_looper("iteration_end", test_pass("end", test_stats, ), ), handle_epoch_end("train", epoch_looper, train_data_looper, csv_result, save_model=True, stats_caller_focus=train_stats), handle_epoch_end("test", epoch_looper, test_data_looper, csv_result, save_model=True, stats_caller_focus=test_stats) ) ).at("iteration_end", csv_result.commit_line(), csv_result.save(), test_stats.reset, train_stats.reset ).at("end", AB.Print("End of training") ) ).dig()


7 hours

1,656 €

EasyRider

About

EasyRider

A Bukkit plugin that improves horses MMO-style by increasing their levels through training. Features

  • The plugin manages two categories of animals:
    • Trackable Animals: llamas.
    • Trainable Animals: Horses, donkeys, mules, skeletal horses and zombie horses.
  • The following capabilities are supported for Trackable Animals:
    • they can be locked,
    • they have an access control list that defines who (besides the owner) can ride them or access their inventory,
    • they can be listed, showing their name, type of animal, equipment, appearance, location, and performance attributes,
    • they can be found, showing their coordinates and the direction to go to reach them.
  • Trainable Animals support all of the capabilities provided for Trackable Animals and can also be trained to improve their performance.
  • Trainable Animals can be trained in three abilities: speed, jump strength and health.
    • Speed: A horses speed increases according to the total horizontal distance travelled on the ground by the horse while carrying a rider.
    • Jump Strength: A horses jump strength increases according to the total horizontal distance travelled in the air by the horse while carrying a rider. Fall distance does not count. You can make the horse jump, ride it off a cliff, or just ride it up and down slopes to improve jump strength.
    • Health: A horses health increases according to the total mass of gold consumed in the form of golden carrots, regular golden apples and Notch apples. The more gold it takes to craft a food item, the more it improves the horses health. Horses eat one food item at a time. You may need to hurt them in order for them to eat golden apples. What does not kill them makes them stronger!
  • Trainable Animals start out at level 1 in each of these three abilities.
  • Trainable Animal attributes (health, jump and speed) improve in discrete

7 hours

1,656 €

Hilics

About

HILICS - Hardware-in-the-loop industrial control system training platform

The Center for Cyberspace Research (CCR) has conducted extensive research in Industrial Control System (ICS) security. ICS architectures utilize a variety of proprietary hardware and software configurations to control and monitor industrial processes and safety systems. Defenders must be familiar with the functionality, requirements and limitations of control systems in order to successfully defend them from cyber-attacks. Hands-on training and experience is crucial for defenders who must be able to interact safely with a given control system. Without this experience, defenders could mistakenly take actions that cause more harm to the system than a basic cyber-attack.
Given the physical nature of ICS architectures, training platforms can be difficult and costly to develop. To address this, the CCR has developed HILICS - a unique hardware-in-the-loop ICS platform designed to support training, education and research. HILICS utilizes a MicroLogix 1100 Programmable Logic Controller (PLC) to introduce students to PLC operating basics and the associated programming languages. This PLC is a low-cost commercial product that provides a representative set of features to give students exposure to control system functionality. The HILICS platform is a custom hardware-in-the-loop system that enables trainers to incorporate multiple physical process simulations and expose students to a range of control system applications. The platform is designed to be flexible and scalable, allowing for varying class sizes and supporting various control system applications.


7 hours

1,656 €

LinearRegression01

About

LinearRegression01

the course discusses linear regression model training for economic income and consumption using sklearn and data visualization with matplotlib.

lr_ml.py

Functions in the code are:

def evaluateModel(self, model, test_data, features, labels) # evaluate model def visualizeModel(self, model, data, feature_names, label_names, error, score) # visualize model def trainModel(self, train_data, feature_names, label_names) # train model def linearModel(self, data, feature_names, label_names, split_ratio) # entry def readData(self, path) # read data from csv file


7 hours

1,656 €

Algorithm Training Java

About

algorithm-training-java

Techopedia explains Algorithm: An algorithm is a detailed series of instructions for carrying out an operation or solving a problem. In a non-technical approach, we use algorithms in everyday tasks, such as a recipe to bake a cake or a do-it-yourself handbook. Technically, computers use algorithms to list the detailed instructions for carrying out an operation. For example, to compute an employees paycheck, the computer uses an algorithm. To accomplish this task, appropriate data must be entered into the system. I develop myself to learn simple but important algorithms, I write with java.


7 hours

1,656 €

AI Accelerators Quality

About

AI_Accelerators_Quality

AI/ML Accelerator sample for training and inferencing very large quantities of similar-ish things in a cost effective manner. Based on experience for real time quality detection or produced things in the manufacturing space.

Introduction

Architecture Overview

ML Overview

Inference Azure Function Overview

Distributed Package Overview


7 hours

1,656 €

Git Training Kit

About

Git and GitHub Training Kit

Sourced by Active Specialized Support Group (ASS-G)

Index

  1. What Is Version Control?
  2. What Is Git?
  3. Getting Started
  4. Configure tooling
  5. Create repositories
  6. Make changes
  7. Group changes
  8. Review history
  9. Redo commits
  10. Synchronize changes
  11. Workflow Summary

What Is Version Control?

Version control helps developers track and manage changes to a software projects code. As a software project grows, version control becomes essential. Take WordPress At this point, WordPress is a pretty big project. If a core developer wanted to work on one specific part of the WordPress codebase, it wouldnt be safe or efficient to have them directly edit the official source code. Instead, version control lets developers safely work through branching and merging. With branching, a developer duplicates part of the source code (called the repository). The developer can then safely make changes to that part of the code without affecting the rest of the project. Then, once the developer gets his or her part of the code working properly, he or she can merge that code back into the main source code to make it official. All of these changes are then tracked and can be reverted if need be.

What Is Git?

Git is a specific open-source version control system created by Linus Torvalds in 2005. Specifically, Git is a distributed version control system, which means that the entire codebase and history is available on every developers computer, which allows for easy branching and merging. According to a Stack Overflow developer survey, over 87% of developers use Git.


7 hours

1,656 €

ClassificationProb

About

ClassificationProb

The task at hand is a multi-class classification problem, for which both a training and a test set are provided as csv files.

Writing out the thought process:

  • The first step is to read through and understand what the problem parameters are pointing towards.
  • With the given instructions we know:
    • The problem is classification and not regression
    • The target is 4 possible outcomes
    • data is split into separate train and test csv
  • Our second step will be to clean the data enough to run it through a model
  • Then we establish a baseline for predictions
  • Next step will be to quickly get a non-engineered Logistic Regression up
  • From there we will begin to examine data to see if feature engineering is a viable strategy
  • Begin to explore data visually

7 hours

1,656 €

Open Go Bot

About

A platform for designing, training and sharing AI models

Allows to design deep-learnings models for various problems, training them in the browser and sharing them with others. This project is still work in progress. That means some important features are still missing:

  • Deployment to Cloud (AWS or GCE)
  • OpenAuth integration
  • Sync models (inclusive weights) and share
  • Generic architecture in order to support multiple scenarios Finished features:
  • Mnist scenario
  • Inspecting weights and activations
  • Editing graphs

    Scenarios

    MNIST

    A large database of handwritten digits that is commonly used for training various image processing systems.


7 hours

1,656 €

Chasers Of The Lost Data

About

Data Chaser

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:


7 hours

1,656 €

Crowdastro

About

crowdastro

This project aims to develop a machine learned method for cross-identifying radio objects and their host galaxies, using crowdsourced labels from the Radio Galaxy Zoo. |PyPI| |Travis-CI| |Documentation Status| |DOI| For setup details, see the documentation on Read the Docs. For a brief description of each notebook, see the documentation here. The cross-identification dataset is available on Zenodo.


7 hours

1,656 €

Api Design

About

Application Programming Interface

Modern applications use web API to communicate together. Web APIs point out a way to communicate through a protocol over a socket (http, websocket, protobuf) synchronously or asynchronously. IT industry uses many standards or protocols : SOAP/XML, XML-RPC, RESTfull/JSON, REST-RPC/JSON, ... The last one is the most used nowadays but there also emerging tehcnologies : Facebook GraphQL, Netflix Falcor or Google grpc.io are gaining adepts since few years During this training we will learn to create a REST/Json API with Node.js, serialiaze data in a database, secure your API and deploy it on a PaaS provider.

Specification :

Our project is to build a simple blog backend.


7 hours

1,656 €

Mmtf Workshop 2017

About

Structural Bioinformatics Training Workshop & Hackathon 2017

Application of Big Data Technology and 3D Visualization, San Diego Supercomputer Center, UC San Diego, June 26-28, 2017 This 3-day hands-on workshop introduces participants to the development of fast and scalable structural bioinformatics methods using state-of-the-art Big Data technologies and Web-GL 3D visualization (). The first two days of the workshop combine lectures, hands-on applications, and programming sessions. On the third day participants apply the new technologies to their own projects. This workshop is held at the University of California, San Diego and hosted by the Structural Bioinformatics Laboratory at SDSC in collaboration with the RCSB Protein Data Bank. Workshop registration page:
(registration is closed) A second workshop will be held early 2018. For question about this workshop or to preregister for the second workshop:

Workshop Outcomes

Software Installation


7 hours

1,656 €

GAE Bag Of Words

About

GAE-Bag-of-Words (GAE-BoW) is a Natural Languange Processing (NLP) model developed during an hackathon project that took place in Salerno (Naples-Italy). The challenge concerned the development of an hybrid mobile application to lead young students in finding their training and professional paths, promoting innovative ways that link the academic and the business world. I and my colleagues developed in 24h a prototype mobile application, that based on a Machine Learning technique wouldve helped new students to decide their own study affinity area. In practice, we firstly invited the students to fill up a survey, in which we presented different questions regarding: topics of study, hobby, types of books, etc. Secondly, we adopted a NLP model (BoW) to process the input data (train set) and then we trained a ML model able to list several disciplines for which a studen is most prone to. The ML model is learned on the basis of about 1,000 surveys, submitted by students of different areas of study. We have won the first prize, which then has provided the complete development of the mobile application.


7 hours

1,656 €

ANN On Churn Modelling Dataset Using Keras

About

Artificial Neural Network

I implemented a simple ANN on the Churn Modelling Dataset and then after training tested on a single data to predict values The idea of ANNs is based on the belief that working of human brain by making the right connections, can be imitated using silicon and wires as living neurons and dendrites. The human brain is composed of 86 billion nerve cells called neurons. They are connected to other thousand cells by Axons. Stimuli from external environment or inputs from sensory organs are accepted by dendrites. These inputs create electric impulses, which quickly travel through the neural network. A neuron can then send the message to other neuron to handle the issue or does not send it forward. Structure of Neuron ANNs are composed of multiple nodes, which imitate biological neurons of human brain. The neurons are connected by links and they interact with each other. The nodes can take input data and perform simple operations on the data. The result of these operations is passed to other neurons. The output at each node is called its activation or node value. Each link is associated with weight. ANNs are capable of learning, which takes place by altering weight values. The following illustration shows a simple ANN A Typical ANN Types of Artificial Neural Networks There are two Artificial Neural Network topologies FeedForward and Feedback. FeedForward ANN The information flow is unidirectional. A unit sends information to other unit from which it does not receive any information. There are no feedback loops. They are used in pattern generation/recognition/classification. They have fixed inputs and outputs. FeedForward ANN FeedBack ANN Here, feedback loops are allowed. They are used in content addressable memories. FeedBack ANN Working of ANNs In the topology diagrams shown, each arrow represents a connection between two neurons and indicates the pathway for the flow of information. Each connection has a weight, an integer number that controls the signal between the two neurons. If the network generates a good or desired output, there is no need to adjust the weights. However, if the network generates a poor or undesired output or an error, then the system alters the weights in order to improve subsequent results. Machine Learning in ANNs ANNs are capable of learning and they need to be trained. There are several learning strategies Supervised Learning It involves a teacher that is scholar than the ANN itself. For example, the teacher feeds some example data about which the teacher already knows the answers. For example, pattern recognizing. The ANN comes up with guesses while recognizing. Then the teacher provides the ANN with the answers. The network then compares it guesses with the teachers correct answers and makes adjustments according to errors. Unsupervised Learning It is required when there is no example data set with known answers. For example, searching for a hidden pattern. In this case, clustering i.e. dividing a set of elements into groups according to some unknown pattern is carried out based on the existing data sets present. Reinforcement Learning This strategy built on observation. The ANN makes a decision by observing its environment. If the observation is negative, the network adjusts its weights to be able to make a different required decision the next time. Back Propagation Algorithm It is the training or learning algorithm. It learns by example. If you submit to the algorithm the example of what you want the network to do, it changes the networks weights so that it can produce desired output for a particular input on finishing the training. Back Propagation networks are ideal for simple Pattern Recognition and Mapping Tasks.


7 hours

1,656 €

FlashCards

About

Flash Cards

A mobile app for long-term memory training using sets of flash cards.

Future Development

  • Create decks based on subject
  • Combine & shuffle multiple decks together for the student to train long-term memory
  • Use text and images on one side of the card to convey an idea to the student, and have the answer waiting for them on the back of the card in plain text
  • Shuffle cards on demand
  • Set up a per-card timer to force the student to answer quickly
  • Track training history
    • correct/incorrect answers
    • avg. card display time
    • card display time as a graph for the entire deck
    • individual card display count

7 hours

1,656 €

Race Ready

About

Race-Ready

A web app for tracking your race training progress to make sure you're ready for race day This Node.js fitness app tracks users marathon training progress by accessing their running data through the Strava API. Race Ready uses Express, Sequelize, and Passport on the backend and React and React-Redux on the front. This app is still a work in progress.


7 hours

1,656 €

Numnormalize

About

Number normalize

Instalation

$ npm i numnormalize --save The need for the normalization of data samples is conditioned by the very nature of the variables used in neural network models. Being different in physical sense, they can often differ greatly in absolute values. So, for example, the sample can contain both concentration, measured in tenths or hundredths of percent, and pressure in hundreds of thousands of pascals. Normalization of data allows you to bring all used numerical values of variables to the same area of their change, which makes it possible to bring them together in one neural network model. In order to normalize the data, you need to know exactly the limits of the changes in the values of the corresponding variables (minimum and maximum theoretically possible values). Then the limits of the normalization interval will correspond to them. When it is impossible to set the limits of variable changes precisely, they are set taking into account the minimum and maximum values in the available data sample.


7 hours

1,656 €

Snapshot Training

About

Snapshot Training for Jest

Based on examples provided by the Jest project, this project will teach you about how to use snapshot testing and how we can automate our assertions using it.

Enzyme: Rule of thumb

  • Shallow Rendering: Always start with shallow()
  • Full Rendering: Use mount() for lifecycle methods and optionally children
  • Static Rendering: Use render() to test children with if you don't care about lifecycle methods

    Snapshots can be used to test

  • Text
  • Objects
  • Arrays
  • DOM

7 hours

1,656 €

Runner Training

About

Runner-Training

Overview

It is important for runners to get adequate training before taking on long distance running events. Finding a training plan to match your goal distance is a good first step. However, even after youve found a good training plan, it is sometimes difficult to get motivated to run some of the long runs. That is when it can be helpful to find local races or running groups to run with. I am creating an app that will help users find training plans to match their goals. In addition, this app will help users find races and running groups that fit into their training schedule. Each user will have a personal calendar populated with their training runs as well as any race or running group options available. This idea stems from how I build my training schedule for ultramarathons. I have run distances of 50 and 100 miles using this strategy.

Features


7 hours

1,656 €

Person Detection

About

Person Identification using Opencv library

Person detection program learns to identify a individual by learning on the training data provided. It then is able to predict the person correctly when a new image of the person is provided as a test data.

Primary libraries used:

  1. opencv2
  2. tkinter
  3. PIL

    Folder structure:

    There are folders:

  4. training-data
  5. test-data Inside training data we need to create folders with prefix "person". Example: "person1" folder can contain my images for it to train. "person2" folder may contain a second person's images and so on.

7 hours

1,656 €

JNN

About

JNN

Java Library for neural networks with evolutionary training there is a java and c# version

  • java fully tested (needs "Apache common math" for CPU and "JCuda" for Nvidia GPU)
  • C# version in beta because i didn't test it (and is based on a really old Java version of this lib)

    Example

    The Java version has an Example, where the black rectangle needs to eat the green food and avoid the red enemies

    api

  • Setup
  • Training
  • save NN
  • info

7 hours

1,656 €


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Is Programming X a good field?


For now, there are tremendous work opportunities for various IT fields. Most of the courses in Programming X 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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