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Programming X Training


Know 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

$1,990

Explore 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

$1,990

Learn 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

$1,990

Know 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

$1,990

Learn 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

$1,990

Know 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

$1,990

Know 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

$1,990

Basics of 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

$1,990

Explore 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

$1,990

Discover 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

$1,990

Learn 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

$1,990

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

$1,990

Discover 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

$1,990

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

$1,990

Learn 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

$1,990

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

$1,990

Learn 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

$1,990

Discover 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

$1,990

Know 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

$1,990

Basics of 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

$1,990

Learn 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

$1,990

Explore 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

$1,990

Learn 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

$1,990

Know 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

$1,990

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

$1,990

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

$1,990

Discover 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

$1,990

Basics of 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

$1,990

Explore Blob Customvision Trainer

About

Blob Storage to Custom Vision Uploader

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

$1,990

Discover 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

$1,990


Is learning Programming X hard?


In the field of Programming X 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.


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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