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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.
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!
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).
This is the code companion to a multi-part series on HelpX:
The main parts of the project are:
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
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.
God of beginnins, time and trasitions...
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 ?
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)
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...
Training bot allows managers of teams to send notifications to their teammates on a predefined schedule.
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.
Simple python program that helps pianists practice and visualize their dynamic expression in real time.
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:
Utilizes python-rtmidi to capture midi messages in realtime
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.
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.
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.
Pytorch code for training imagenet with fp16
Get to know FRINX software and solutions hands on through a series of labs.
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.
Evaluation on class "dog" on PASCAL VOC 2012 dataset:
Very simple example source code of how to train vectors in Brain.JS with Neural Network.
You can find preferences below require statements at index.js.
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.
data/model.txt.loadFromPreviousModel to false and test this is running correctly.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.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.
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):
Compared to other robotic simulations intended for sim-to-real transfer, the dependencies are light:
gympybullet >= 2.4.0pyquaternion
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.AppFeedbackSystem provide your app with dialog with feedback options; Frequently Asked Questions, Feature Request, General Feedback, Bug Report, & Contact Us.
I have learned a lot in training in this library and I have stopped developing it since I learned what I need.
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:
Must download:
Download the zip from github, unzip
Open Epic Game Launcher, and click the LAUNCH Unreal Enginge in the top right-hand corner
Select the "Protolith" file from the unzipped file package
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.
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.
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:
GCP in a bash shell is an educational repository to share study resources to learn [Google Cloud Platform (GCP)][1].
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.
Unedited notes available in [docs/cert-pca/][3] Unedited notes available in [docs/cert-ace/][6]
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.
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.
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).
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:
Abyme is a tool for writing Deep and Sophisticated (Training) Loops. Training loops involve a lot cuisine:
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()
In the field of Programming Detection Tool 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 Detection Tool 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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