Instructor-led live Computer Programming training courses demonstrate through interactive hands-on practice the fundamentals and advanced topics of Programming. Experience the remote live training by way of interactive and remote desktop led by a human being!
Live Instructor Led Online Training Programming courses is delivered using an interactive remote desktop! .
During the course each participant will be able to perform Programming exercises on their remote desktop provided by Qwikcourse.
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This is a 3-day training covering principles of modelling, NAF, UPDM and use of MagicDraw following a case study, which demonstrates a typical defence architecture approach.
The course:
Is based on a consistent modelling case study.
Audience:
Methods:
Course Materials:
Certificates:
This course will introduce various concepts in computer programming. There are some simplifications in the explanations below. The purpose of programming is to tell the computer what to do. Computers are better at doing some things than you are like a chef is better at cooking than you are. It's easier to tell a chef to cook you a meal than to cook it yourself. The more precise you are in your request, the more your meal will turn out how you like it. In most scenarios like this in real life, small amounts of ambiguity and misinterpretation are acceptable. Perhaps the chef will boil your potato before mashing it instead of baking it. With computers, however, ambiguity is rarely acceptable. Programs are so complex that if the computer just guessed at the meaning of ambiguous or vague requests, it might cause problems so subtle that you'd never find them. Programming languages, therefore, have been designed to accept only completely clear and unambiguous statements. The program might still have problems, but the blame is then squarely on you, not on the computer's guess. Much of the difficulty in programming comes from having to be perfectly precise and detailed about everything instead of just giving high-level instructions. Some languages require total and complete detail about everything. C and C++ are such languages and are called low-level languages. Other languages will make all sorts of assumptions, and this lets the programmer specify less detail. Python and Basic are such languages and are called high-level languages. In general, high-level languages are easier to program but give you less control. Control is sometimes important, for example, if you want your program to run as quickly as possible. Most of the time total control and speed isn't necessary, and as computers get faster high-level languages become more popular. Here we will deal exclusively with high-level languages. Low-level languages are generally similar except there are often more lines of code to write to accomplish the same thing.
Object orientation is a way of programming that bears a remarkable resemblance to the way electric devices have evolved. This page will explain the problems of the past, and how these were solved by components (in electric devices) and objects in programs. The first electric devices were one web of components. If you looked into the interior of an ancient radio, everything was connected. Only a few things could be disconnected: the power plug and maybe external speakers. Sometimes these devices came with an electrical scheme, which had everything in a detailed manner. You could look for hours at such a scheme and discover all sorts of functions in that scheme, like radio reception, amplification, tone control, etc. The first computer programs had the same structure. A program written in assembler or in Basic could easily span hundreds and thousands of code lines. It had to because all the details had to be provided in one code file. Of course, a developer was really proud those days if he managed to make a working program with a decent set of features, but maintenance was hard. And if you wanted another program, you had to write it from scratch. Reusing code from previous programs was not really impossible (you could copy some subroutines), but it was not exactly easy.
Content
Parallel computing and computer clusters are two separate subjects. However, there is a very large cross over between the two which would make for large quantities of duplicated material should they be described separately. The aim of This course is to provide a solid foundation for understanding all aspects of both parallel computing and computing clusters. It will begin by providing an overview of both of the terms used in the course's title and then breaking down existing hardware and software practices to see how they fit into the overall picture. The text will continue on to describe common features of parallel computing & computer clusters in their basic forms - sometimes they are features not readily associated with the field such as the task scheduling in everyday operating systems.
A Programmable Logic Controller, or PLC, is more or less a small computer with a built-in operating system (OS). This OS is highly specialized and optimized to handle incoming events in real time, i.e., at the time of their occurrence. The PLC has input lines, to which sensors are connected to notify of events (such as temperature above/below a certain level, liquid level reached, etc.), and output lines, to which actuators are connected to affect or signal reactions to the incoming events (such as start an engine, open/close a valve, and so on). The system is user programmable. It uses a language called "Relay Ladder" or RLL (Relay Ladder Logic). The name of this language implies that the control logic of the earlier days, which was built from relays, is being simulated. Some other languages used include: A programmable logic controller, PLC, or programmable controller is a digital computer used for automation of typically industrial electromechanical processes, such as control of machinery on factory assembly lines, amusement rides, or light fixtures. PLCs are used in many machines, in many industries. PLCs are designed for multiple arrangements of digital and analog inputs and outputs, extended temperature ranges, immunity to electrical noise, and resistance to vibration and impact. Programs to control machine operation are typically stored in battery-backed-up or non-volatile memory. A PLC is an example of a "hard" real-time system since output results must be produced in response to input conditions within a limited time, otherwise unintended operation will result.
This course will cover the design and implementation of programmable logic devices (PLDs) using the Verilog, VHDL, and System C Hardware description languages. It is not meant to be a comprehensive reference to these languages, but more of a quick guide that covers the parts essential to developing effective digital designs. This course will also cover programming a variety of specific programmable devices, such as FPGA and CPLD devices. Contents intended to be covered are: Programming HDL, Concurrent programming and synthesis. A Previous knowledge of programming a high-level language is assumed. Knowledge of Semiconductors is helpful but not necessary. Previous experience or knowledge of Digital Circuits is must.
A high-quality software application makes its users happy. They enjoy using it and it doesn't get in their ways. It yields the right results quickly, without requiring workarounds for bugs, does not crash or hogs the system, and allows the users to go on with the rest of their lives. Either the program is invisible to them and they don't think about using it, or it works so well that they enjoy using it and possibly comment about it to their friends. On the other hand, a software application of poor quality annoys, irritates and/or frustrates its users, or even causes them to lose a lot of time, money or worse. Either it is too slow and they lose patience waiting for it to perform its function. Or it crashes a lot or hogs the system. Or it could look ugly, or have a poorly designed user-interface. Or it has other bugs like those causing data loss. Whatever its faults are, it fails or partially fails to be a useful tool in the hand of the user. As software developers, it is our mission to make sure the software we produce is high-quality so it will perform its function properly without inflicting anguish or loss upon the user.
Proposed Methods to Achieve Quality
This course describes how to install Lino. System requirements. Set up a Python environment. Running your first Lino site. It assumes you are familiar with the Linux shell at least for basic file operations like ls, cp, mkdir, rmdir, file permissions, environment variables, bash scripts etc. Otherwise we suggest to learn about Working in a UNIX shell. Lino theoretically works under Python 3, but we currently still recommend Python 2. If you just want it to work, then choose Python 2. Otherwise consider giving it a try under Python 3 and report your experiences. We assume you have pip installed. pip is not automatically bundled with Python 2, but it has become the de facto standard. Here is how to install it on Debian: You will need to install git on your computer to get the source files:
Object detection (OB) is one of the computer technologies which are connected to the image processing and computer vision spectra of artificial intelligence. OB interacts with detecting instances of an object such as human faces, buildings, trees, cars, etc. The primary aim of face detection algorithms are to determine whether there are any human faces in an image or not. This repo primarly contains:
dlib, opencv and tensorflow implementation.Development System - A system I created that makes it possible to test several cool functions with face detections.
Coding Demonstrations: Programming for Social Science Research
Computational methods for collecting, cleaning and analysing data are an increasingly important component of a social scientists toolkit. Central to engaging in these methods is the ability to write readable and effective code using a programming language.
The following topics are covered under this training series:
The Training Course - including recordings, slides, and sample Python code - can be found in the following folders:
We are grateful to UKRI through the Economic and Social Research Council for their generous funding of this training series.
Being a Computational Social Scientist
Scientific research and teaching is increasingly influenced by computational tools, methods and paradigms. The social sciences are no different, with many new forms of social data only available through computational means (Kitchen, 2014). While to some degree social science research has always been marked by technological approaches, the field of computational social science involves the use of tools, data and methods that require a different skill set and mindset.
The following topics are covered under this training series:
The Training Course - including webinar recordings, slides, and sample Python code - can be found in the following folders:
We are grateful to UKRI through the Economic and Social Research Council for their generous funding of this training series.
Microsoft Graph Training Module - Build Ruby on Rails apps with Microsoft Graph
This module will introduce you to working with Microsoft Graph to access data in Office 365 by building Ruby on Rails web applications.
Lab - Build Ruby on Rails apps with Microsoft Graph
In this lab you will create a Ruby on Rails web application using the Microsoft identity platform authentication endpoint to access data in Office 365 using Microsoft Graph.
Completed sample
If you just want the completed sample generated by following this lab, you can find it here.
This is the Web Based learning website build with Spring 5 and latest web technology specially designed for the student to learn different things in a single website. Main focus is given on live training conduct by teacher to the student and live video streaming is based on the Java websocket protocol. This is build for Tribhuvan University of NEPAL.
Microsoft Graph Training Module - Build iOS apps with Swift and the Microsoft Graph SDK
This module will introduce you to working with the Microsoft Graph SDK for Objective-C in creating a Swift-based iOS application to access data in Office 365.
Lab - Build iOS apps with Swift and the Microsoft Graph SDK
In this lab you will create an iOS application using Swift, configured with Azure Active Directory (Azure AD) for authentication & authorization, that accesses data in Office 365 using the Microsoft Graph SDK.
Completed sample
If you just want the completed sample generated by following this lab, you can find it here.
Microsoft Graph Training Module - Build ASP.NET Core apps with Microsoft Graph
This module will introduce you to working with the Microsoft Graph .NET SDK in creating an ASP.NET Core MVC web application to access data in Office 365.
In this lab you will create an ASP.NET Core MVC application, configured with Azure Active Directory (Azure AD) for authentication & authorization, that accesses data in Office 365 using the Microsoft Graph .NET SDK.
If you just want the completed sample generated by following this lab, you can find it here.
Microsoft Graph Training Module - Build .NET Core apps with the Microsoft Graph SDK
This module will introduce you to working with the Microsoft Graph SDK to access data in Office 365 by building .NET Core applications.
Lab - Build .NET Core apps with the Microsoft Graph SDK
In this lab you will create a console application using the Microsoft Authentication Library (MSAL) to access data in Office 365 using the Microsoft Graph.
Completed sample
If you just want the completed sample generated by following this lab, you can find it here.
MyVision is a free online image annotation tool used for generating computer vision based ML training data. It is designed with the user in mind, offering features to speed up the labelling process and help maintain workflows with large datasets.
Draw bounding boxes and polygons to label your objects:
Polygon manipulation is enriched with additional features to edit, remove and add new points:
Supported dataset formats:
Annotating objects can be a difficult task... You can skip all the hard work and use a pre-trained machine learning model to automatically annotate the objects for you. MyVision leverages the popular 'COCO-SSD' model to generate bounding boxes for your images and by operating locally on your browser - retain all data within the privacy of your computer:
You can import existing annotation projects and continue working on them in MyVision. This process can also be used to convert datasets from one format to another:
No setup is required to run this project, open the index.html file and you are all set!
Bot template project with code samples on how to use the features offered by Symphony Bot SDK.
Getting Started
Command handling
Event handling
Receiving notifications
Symphony elements samples
Plugging in an extension app
Extending monitoring endpoints
Data Science in Production
This course covers the tools and techniques commonly utilized for production machine learning in industry. Students learn how to provide web interfaces for training machine learning or deep learning models with Flask and Docker. Students will deploy models in the cloud through Amazon Web Services, gather and process data from the web, and display information for consumption in advanced web applications using Plotly and D3.js. The students use PySpark to make querying even the largest data stores manageable.
Explain why students should care to learn the material presented in this class.
Weeks to Completion: 7 Total Seat Hours: 37.5 hours Total Out-of-Class Hours: 75 hours Total Hours: 112.5 hours Units: 3 units Delivery Method: Residential Class Sessions: 14 classes, 7 labs
Vagrant is an open-source software product for building and maintaining portable virtual software development environments; e.g., for VirtualBox, KVM, Hyper-V, Docker containers, VMware, and AWS. It tries to simplify the software configuration management of virtualizations in order to increase development productivity. Vagrant is written in the Ruby language, but its ecosystem supports development in a few languages.
On-Line Fraud Detection involves identifying a fraud as soon as it has been perpetrated into the system. This technique is only successful by having a training algorithm that can produce a model suitable to be used by a real-time detector. In this project, we will focus on fraud detection for credit card transactions, using Markov chains to train the model off-line and a parallel implementation of a concurrent queue for on-line detection.
Did this project as a part of requirements for graduation of the Udacity Deep Learning Nanodegree. Constructed a Recurrent Neural Network (LSTM) on top of an embedding layer and trained it on the BOW encoded IMDB dataset containing movie review for detecting sentiment of a movie review. Created the training job, model and deployed the model on AWS SageMaker service. Gave access of the deployed model to a simple web app that can take input review as text from an user and give the sentiment of the review as output by creating a Lambda function and API Gateway and giving the web app access to the API Gateway.
Interactive Voice Maths training skill for Alexa. Maths Mentals Skill Maths Mentals is a maths quiz game. You can choose between 5 levels, with 1 being easiest and 5 the hardest. The quiz includes addition, subtraction, multiplication and division with varying levels of difficulty. The questions are dynamic and every quiz is different. Maths Mentals is suitable for kids from kindergarten all the way to primary school.
In the last few decades, the research on face recognition has become a trendy topic. Significant developments have been made by various research in this field. The definition of face recognition can be expressed as a task using biometric features to recognize the human face. The tasks of face recognition can be divided into three types: Face verification (are they the same person), face identification (who is this person), and face clustering (find common people among these faces). To check what we have learned in this semester, we decided to train a face verification model in a real-world dataset with deep learning approaches. The dataset we used is the Labeled Faces in the Wild Database (LFW). The LFW database is a well-known in face recognition field. It contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person in the picture. There are 1,680 individuals have two or more distinct photos in the dataset. Our method followed the general machine learning workflow: starting with data preprocessing, building model from baseline then improving the model step by step to gain a better performance on the test data. This course aims to train a deep learning model for face verification task. Similar to other machine learning tasks, our method is a purely data driven method as the faces are represented by the pixels. The deep learning network we used is the convolutional neural network. Although face recognition model has been developed very sophisticated, our project is proposed to check the deep learning knowledge we have learnt in this semester. Thus, this course can be regard as a deep learning model training for face recognition in action. With limited computational power, we managed to adjust the model to its best performance.
Padertorch is designed to simplify the training of deep learning models written with PyTorch. While focusing on speech and audio processing, it is not limited to these application areas. This repository is currently under construction. The examples in contrib/examples are only working in the Paderborn NT environment
Highlights
padertorch.Trainer
Logging:
The review of the model returns a dictionary that will be logged and visualized via 'tensorboard'. The keys define the logging type (e.g. scalars).
As logging backend we use TensorboardX to generate a tfevents file that can be visualized from a tensorboard.
Dataset type:
lazy_dataset.Dataset, torch.utils.data.DataLoader and other iterables...
Validation:
The ValidationHook runs periodically and and logs the validations results.
Learning rate decay with backoff:
The ValidationHook has also parameters to do a learning rate with backoff.
Test run:
The trainer has a test run function to train the model for few iterations and test if
the model is executable (burn test)
the validation is deterministic/reproducable
the model changes the parameter in the training
Hooks:
The hooks are used to extend the basic features of the trainer. Usually the user dont rearly care about the hooks. By default a SummaryHook, a CheckpointHook and a StopTrainingHook is registerd. So the user only need to register a ValidationHook
Checkpointing:
The parameters of the model and the state of the trainer are periodically saved. The intervall can be specified with the checkpoint_trigger (The units are epoch and iteration)
Virtual minibatch:
The Trainer usually do not know if the model is trained with a single example or multiple examples (minibatch), because the exaples that are yielded from the dataset are directly forwarded to the model.
When the virtual_minibatch_size option is larger than one, the trainer calls the forward and backward step virtual_minibatch_size times before applying the gradients. This increases the minibatch size, while the memory consumption stays similar.
ADAS is short for Adaptive Step Size, it's an optimizer that unlike other optimizers that just normalize the derivatives, it fine-tunes the step size, truly making step size scheduling obsolete.
This is a graph of ADAS (blue) and ADAM (orange)'s inaccuracy percentages in log scale (y-axis) over epochs (x-axis) on MNIST's training dataset using shallow network of 64 hidden nodes. It can be seen that in the start ADAS is ~2x faster than ADAM, and while ADAM slows down, ADAS converages to 0% inaccuracy (AKA 100% accuracy), in 24 iterations exactly, and since then never diverging. To see how ADAM was tested see/run the python script ./adam.py, it uses tensorflow. ADAS was compared against other optimizers too (AdaGrad, AdaDelta, RMSprop, Adamax, Nadam) in tensorflow, and none of them showed better results than ADAM, so their performance was left out of this graph. Increasing ADAM's step size improved the performance in the short-term, but made it worse in the long-term, and vice versa for decreasing it's step size.
This section explains how ADAS optimizes step size. The problem of finding the optimal step size formulates itself into optimizing f(x + f'(x) * step-size) by step-size. Which is translated into this formula that updates the step-size on the fly: step-size(n+1) = step-size(n) + f'(x) * f'(x + f'(x) * step_size(n)). In english it means optimize step size so the loss decreases the most with each weights update. The final formula makes sense, because whenever x is updated in the same direction, the step-size should increase because we didn't make a large enough step, and vice versa for opposite. You may notice that there's a critical problem in computing the above formula, it requires evaluation of the gradient on the entire dataset twice for each update of step-size, which is quite expensive. To overcome the above problem, compute a running average of x's derivative in SGD-context, this represents the f'(x) in the formula, and for each SGD update to x, its derivative represents the f'(x + f'(x) * step_size(n)), and then update the step-size according to the formula.
mpify is a simple API to launch a "target function" parallelly on a group of ranked processes via a single blocking call. It overcomes the few quirks when multiple python processes meets interactive Jupyter/IPython meets multiple CUDA GPUs, and has the following features:
Original: mpify-ed:
The examples/ directory contains:
It is an audio dataSet generator. For training Neural Networks Just put the youtube video links in the json array under "links" key, and $.wav$ file with each pronunced word will be extracted. In order to extract each word the video should have automatic-generated subtitles. For better extractions, the videos should have audio with no/low noise and silence between words.
In the field of Programming 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 Programming 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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