Live Instructor Led Online Training Tools in Programming III courses is delivered using an interactive remote desktop! .
During the course each participant will be able to perform Tools in Programming III exercises on their remote desktop provided by Qwikcourse.
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Holo Assisted Training
A holographic assembly line training prototype based on Microsoft HoloLens. In this demo, you are instructed on how to assemble a few parts into an engine. Make sure you are fast and dont make mistakes! You will receive a final score based on your performance.
GloboNetworkAPI
Globo NetworkAPI is a REST API that manages IP networking resources. It is supposed to be not just an IPAM, but a centralized point of network control, allowing documentation from physical and logical network and starting configuration requests to equipments. Globo NetworkAPI is made to support a Web User Interface features, exposing its functionality to be used with any other client. This web tool helps network administrator manage and automate networking resources (routers, switches and load balancers) and document logical and physical networking. They were created to be vendor agnostic and to support different orchestrators and environments without loosing the centralized view of all network resources allocated. It was not created to be and inventory database, so it does not have CMDB functionalities.
Features
LDAP authentication
Supports cabling documentation (including patch-panels/DIOs)
Separated Layer 2 and Layer 3 documentation (vlan/network)
IPv4 and IPv6 support
Automatic allocation of Vlans, Networks and IPs
ACL (access control list) automation (documentation/versioning/applying)
Load-Balancer support
Automated deploy of allocated resources on switches, routers and load balancers
Load balancers management
Expandable plugins for automating configuration
the course discusses scripts to process public voice recognition training data sets into a format suitable to training. Currently supported corpora: TED, LibriVox, VCTK Format: Directory structure with pairs of .wav file for the audio (16khz, 1 channel, 16-bit signed) and a .txt file for the transcription. Each .wav/.txt pair corresponds to a sentence or sentence part.
Robust Gram Word Embeddings 1.What is it? This is an implementation of the Robust Gram (RG) Word embeddings and few evaluation scripts for Word Similarity measurement. 2.When should I use it? Robust Gram is especially powerful when training data is limited. Competetive models such as SkipGram-Word2Vec risk losing their generalization abilities due to the complexity of the models and the overfitting to finite data. Regularized formulation of Robust Gram penalizes overfitting by suppressing the disparity between target and context embeddings. It is shown to be more robust to variations in the training set, and correlates well to human similarities in a set of word similarity tasks. If you think the code is useful or you use it for research purposes, please cite the following work: 3.How to use it? Given a text corpus, the Robust Gram tool learns a vector for every word in the vocabulary using a shallow network architecture. The user should to specify the following: Run the demo script: We provide the minimal demo-rw-sim.sh script. It downloads a small (100MB) Wikipedia text corpus from the web, and trains a word vector model. After the training is finished, it displays the Spearman Correlation Coefficient for Rare Words dataset. on the default setting Robust Gram should output something like this: Spearman C. (Avg) on RW: 0.378097
Reinforcement learning with Generative Neural Networks
Training Reinforcement Learning agent using derivative of Generative Recurrent Neural Network which models jointly environment and reward. Run "example.py" to see it working. This code requires Chainer and Numpy to be installed. On the high level the code works as follows:
Agent RNN is initialized, probability of agent just outputting random action is set to 1.0.
Agent acts in an environment, generating data about the environment.
Collected data about environment is split evenly into training and validation parts.
Two separete generative RNNs are trained on training and validation parts of data. Any of such generative RNNs can be viewed as a differentiable model of environemnt.
Agent is trained to optimize average reward on training environment using gradient descent over outputs of training environment GAN. Agent training stops when performance on valiadation GAN starts to decrease.
Probability of agent outputing random action is decreased. Repeat from step 2 until terminating criterion (fixed number of iterations).
Programming Challenges
Training programming problems from UVA (Univsersity of valladolid), spoj (Sphere online judge) Just trying to use my spare time with these challenges and I'm entertaining myself with these problems.
If you know a good one, you can pull request it.
If you know a better solution, you can pull request it!
let's have some fun.
Android DisplayingBitmaps Sample
how to load large bitmaps efficiently off the main UI thread, caching bitmaps (both in memory and on disk), managing bitmap memory and displaying bitmaps in UI elements such as ViewPager and ListView/GridView. Introduction This is a sample application for the Android Training class [Displaying Bitmaps Efficiently][1]. It demonstrates how to load large bitmaps efficiently off the main UI thread, caching bitmaps (both in memory and on disk), managing bitmap memory and displaying bitmaps in UI elements such as ViewPager and ListView/GridView. Pre-requisites Screenshots
Hapi.js
Description
Learn how to take your application to the next level. With hapi.js training you will learn about the internals of hapi.js as well as how to use it to enable teams to build web applications and services.
With this training, you will learn
Java-Functional-Training
Exercises for training FP with Java This is a training ground for exercising functional programming with Java, assisted by TotallyLazy. Java 8 SDK is required. Each section contains tests that you can use to verify your implementation.
Recursion, Collections and Higher order functions
These are located in exercises-package. They cover some of the basic but essential topics of functional programming. Implement methods marked with TODO-comment and run tests to see if your implementation works correctly.
Project Euler problems
Have a look at the euler-package. Problem32 is an example solution with comparison to an imperative solution. More exercises without solutions will be added to that package for training later on.
Tests
Make sure you are using Java 8 SDK. Then run ./gradlew test
This app demonstrates many of other features in Angular. The UI is very basic and is not intended to be an example of a good design!
Content
stonegold-secretdb
a demonstration for auto encryption/decryption of db table secret fields in internal training of touch-a-stone-and-turn-it-into-gold
initial code. package stonegold; import java.sql.Connection; import java.sql.DriverManager; import java.sql.PreparedStatement; import java.sql.SQLException; public class Main { public static void main(String[] args) throws ClassNotFoundException, SQLException { Class.forName("org.h2.Driver"); Connection conn = DriverManager.getConnection("jdbc:h2:~/stonegold", "sa", ""); String createTableSql = "create table person(name varchar(10), id_no varchar(18), credit_card varchar(16))";
Marathon Trainer
This program is a Java Swing program meant to provide a visual aid for your marathon training. Given a training plan and a marathon date, this program will provide you with a calendar view of when you'll be training for what. Specifically, once you enter your training plan and launch the application, it'll tell you (for example) that today you'll be running 9 KM at a regular pace. (The paces for the different run types are defined via the training plan.) Requirements Gradle 1.6+ is required to build marathon-trainer.
TrainTimer
Table of Contents
Purpose
How to use it
Limitations
License
Purpose
The TrainTimer is a web application meant to be used offline: in the gym. In my gym is a training circle which has a central clock to schedule the single training units. This clock isn't viewable from every position in the circle. As a remedy I wrote this app (honestly, it was made to learn the use of the application cache). Once started the timer counts down the training time, then the pause time, then again the training time. Both are adjustable on the "settings" ("Einstellungen") page.
Galera-Training
Ansible course for setting up a MariaDB Galera cluster.
SELinux and IPTables rules must be disabled or set up manually on target hosts before running these playbooks.
Put your Galera cluster options into group_vars/galera-training/galera-options.json file.
For each cluster node create a dedicated directory under host_vars/, copy the host_vars/galera-test1.local/galera_options.json there and edit options accordingly.
Include each node's hostname under galera-training group in the hosts file.
Run the playbooks/install_galera_server.yml against the whole server group or individual hosts using 'nodes' variable in the ansible-playbook command line.
Bootstrap the first node and run the create_replication_user.yml against that host.
Start up secondary cluster nodes and check the cluster status.
WinDevWorkshop
Hands on Workshop for Windows 10 Developer Training This workshop is based on the full Windows Dev Camp set of materials, which is in turn based on the "Developer's Guide to Windows 10" which was produced as a set of on-demand video training on both Channel 9 and Microsoft Virtual Academy. The purpose of the Workshop is to provide a more interactive developer training experience that minimizes lectures in favor of more instructor-led lab experiences. Each module contains a minimal PPT deck and one or more ' hands-on labs' for direct delivery in a classroom setting. The day-long agenda consists of the following modules, each 60 minutes split between PPT and Lab
Lemon-Trainer
Linux-Express-Mongo-Node install-configure-build training materials for A100
Tutorials
VirtualBox and Ubuntu installation
Linux filesystem and Bash commands
NodeJS and NPM Installation
MongoDB installation
MongoDB CRUD operations
Training App Iterations
Basic NodeJS web app
A Node call to MongoDB
Node templating and routing with Swig and ExpressJS
A single-page LEMoN web app
A LEMoN app with profile pages
.gitignore
Mobile Applications for Windows
Creating Line-of-business (LoB) Application
Design and implement a native mobile application for Windows 8.1 or Windows 10. The application should be touch-friendly and should provide actual functionality which users can take advantage of. That is, the application must be of value to the end user of a tablet or smartphone, enabling them to consume/produce real content. DO NOT make an application for imaginary or unrealistic tasks
Requirements:
Creativity
Value to the end user
Sfa-targets
The project aims at trying to capture targets vs. actuals for sales organization using SMS and google spreadsheet as they require very little training. This is an open source project and is free to use for anyone. Remember to not checkin your google password in the config file :)
Habit Trainer
A simple Pebble watch app for training habits that require near-continuous attention.
Purpose
Now you can easily train habits that require near-continuous attention! (ex: having good posture) It vibrates periodically. If you were doing a good job, press Up and the timer will wait longer next time. If you forgot, press Select to keep the same interval, or Down to decrease it. Soon you'll start actually remembering!
Version History
1.0.0: First public release on the app store.
Debugging and Performance Analysis Using DTrace
Description
In this session, attendees will be given a few example applications written in Node.js that have bugs and/or performance issues. To do the debugging, we'll be using DTrace and mdb on SmartOS.
With this training, you will learn
applications.
Trainers
[TJ Fontaine]()
[Max Bruning]()
Discord_RPG
Discord bot that allows users to do things typical in RPG games such as fighting, training and leveling up. The game has 3 stats that you can train with stamina which you gain every 5 minutes. Power gives you more damage, speed allows you to get more hits in, and strength gives you more defense while also adding a little bit more damage. As you level up, your health increases and you get an increase in stats each time you train.
Tourism_chatbot_RasaNLU. This project aims at testing RasaNLU by improving intents classification and entity extraction of a tourism chatbot. To achieve that we try two pipelines (spacy_sklearn, tensorflow_embedding) and different components of both pipelines. Therefore, this is a first part towards building a tourism chatbot with Rasa; the second being the training of a Rasa Core model. The notebook contains a test evaluation of both TensorFlow and spacy pipelines with increasing training data sizes. It appears that tensorflow_embedding is much faster and gives a very good accuracy while spacy has scalability problems and gives bad results.
Python 2.7 -- Cuda-8.0 -- TensorFlow 1.4.0 -- Keras 2.1.5
3D Segmentation with Adversarial Networks
This project is inspired in the 3 papers below with implementation in Keras, used in a 3D dataset. The application is the segmentation of the cerebrovascular system in MRA phase contrastusing images. In the segmentor part of the network, I am also using a dice score as a mixed loss function.
Module 1: Security Fundamentals
This module is designed to introduce you to academic concepts in security. By the end of this module, you should be able to understand that security is fundamentally risk management. You will also have developed a vocabulary to talk about different kinds of security threats.
Overview of Security:
Introduction to Computer Security:
Interesting Bonus Materials:
Questions for discussion:
Module 2: Web Application Security
We make a web app. There are very specific and immediate vulnerabilities each developer needs to understand well and defend against. Move slowly through these and finish the hacksplaing exercises on your own terms. This is marathon - not a sprint!
Exercises
Module 3: Cryptography
Redox is a highly networked application. All of that information needs to be secured in transport and at rest. Cryptography is what is lets us do that.
Public key cryptography
Exercises
Module 4: Cloud Infrastructure Security
Final Project
The final project will be a presentation to the team on a recent security topic of interest to you. Here are some resources for cutting edge topics:
Seth is a tool written in Python and Bash to MitM RDP connections by attempting to downgrade the connection in order to extract clear text credentials. It was developed to raise awareness and educate about the importance of properly configured RDP connections in the context of pentests, workshops or talks. The author is Adrian Vollmer (SySS GmbH).
miTester for SIP is an automated SIP testing tool designed and developed to take care of the complex pre-deployment testing of SIP applications easily. This SIP testing tool can be used to simulate SIP call-flows & automate functional, regression tests.
In the field of Tools in Programming III 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 Tools in Programming III 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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