Search Results for "model train design"
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Showing 27 open source projects for "model train design"
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Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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Field Service+ for MS Dynamics 365 & SalesforceResco’s mobile solution streamlines your field service operations with offline work, fast data sync, and powerful tools for frontline workers, all natively integrated into Dynamics 365 and Salesforce.
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LLM101n
LLM101n: Let's build a Storyteller
...The materials favor compact, readable code and incremental steps, so learners can verify each concept before moving on. You’ll see how data pipelines, batching, masking, and positional encodings fit together to train a small GPT-style model end to end. The repo often complements explanations with runnable notebooks or scripts, encouraging experimentation and modification. By the end, the focus is less on polishing a production system and more on internalizing how LLM components interact to produce coherent text. -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines... -
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Hello SQL
Spanish-language course repository that teaches fundamentals of SQL
...It focuses mainly on MySQL for lessons due to its ubiquity in education and professional environments, while also introducing PostgreSQL to broaden learners’ exposure to modern database tooling. The materials emphasize real-world query writing, schema design basics, and the mental model behind SELECT, JOIN, GROUP BY, and subqueries. Learners progress from setup and connection to hands-on exercises that build confidence with CRUD operations and data modeling. The repository’s structure favors incremental learning, with clear folders, references, and exercises you can run locally. It targets absolute beginners as well as developers from other stacks who want a clean, project-based path into SQL. -
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Tux Paint
An award-winning drawing program for children of all ages
Tux Paint is a free, award-winning drawing program originally created for children ages 3 to 12, but enjoyed by all! It combines an easy-to-use interface, fun sound effects, and an encouraging cartoon mascot who guides children as they use the program. You're presented with a blank canvas and a variety of drawing tools to help them be creative. Along with paintbrush, shapes and text, Tux Paint includes a "stamp" feature to add pre-drawn or photographic imagery to pictures, and a set of... -
All-in-one security tool helps you prevent ransomware and breaches.Blumira’s detection and response platform enables faster resolution of threats to help you stop ransomware attacks and prevent data breaches. We surface real threats, providing meaningful findings so you know what to prioritize. With our 3-step rapid response, you can automatically block known threats, use our playbooks for easy remediation, or contact our security team for additional guidance. Our responsive security team helps with onboarding, triage and ongoing consultations to continuously help your organization improve your security coverage.
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Summarize from Feedback
Code for "Learning to summarize from human feedback"
The summarize-from-feedback repository implements the methods from the paper “Learning to Summarize from Human Feedback”. Its purpose is to train a summarization model that better aligns with human preferences by first collecting human feedback (comparisons between summaries) to train a reward model, and then fine-tuning a policy (summarizer) to maximize that learned reward. The code includes different stages: a supervised baseline (i.e. standard summarization training), the reward modeling component, and the reinforcement learning (or preference-based fine-tuning) phase. ... -
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Project: Olympus
An Arduino-based Altitude and IMU Payload for Model Rocketry
Project: Olympus is an engineering project that utilizes an Arduino Nano microcontroller to create an electronic payload that records altitude, g-forces experienced by the rocket, as well as roll rate. Included with the code is a 132 page Project Manual that takes you through the design and construction of the electronic payload. It also includes sections on the construction of the Estes Olympus rocket. A detailed code review of each section of code is included. We take a detailed look... -
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Selfie
An educational software system of a tiny self-compiling C compiler
An educational software system of a tiny self-compiling C compiler, a tiny self-executing RISC-V emulator, and a tiny self-hosting RISC-V hypervisor. Selfie is a project of the Computational Systems Group at the Department of Computer Sciences of the University of Salzburg in Austria. The Selfie Project provides an educational platform for teaching undergraduate and graduate students the design and implementation of programming languages and runtime systems. The focus is on the construction... -
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Arduino Primary Avionics Module (A-PAM)
A foundational avionics system for model rockets
...That meant it needed to be small, fitting everything into a body tube with a diameter no more than 42mm. This would allow it to use “D” or “E” motors. It would need to be flexible. I wanted the design to be a starting point to allow them to conduct real science and it had to be inexpensive. -
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AllenNLP
An open-source NLP research library, built on PyTorch
AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional... -
Comet Backup - Fast, Secure Backup Software for MSPsComet is a flexible backup platform, giving you total control over your backup environment and storage destinations.
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Hello AI World
Guide to deploying deep-learning inference networks
...The tutorial focuses on networks related to computer vision, and includes the use of live cameras. You’ll also get to code your own easy-to-follow recognition program in Python or C++, and train your own DNN models onboard Jetson with PyTorch. Ready to dive into deep learning? It only takes two days. We’ll provide you with all the tools you need, including easy to follow guides, software samples such as TensorRT code, and even pre-trained network models including ImageNet and DetectNet examples. Follow these directions to integrate deep learning into your platform of choice and quickly develop a proof-of-concept design. -
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Catalyst
Accelerated deep learning R&D
...It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new. Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. ... -
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Deep Learning 500 Questions
500 Questions on Deep Learning using a question-and-answer format
...Later chapters explore classic neural network structures such as CNNs, RNNs, and GANs, as well as key applications in computer vision like object detection and image segmentation. The resource also delves into optimization methods, including transfer learning, network architecture design, hyperparameter tuning, model compression, and acceleration techniques. -
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EduData
Datasets in Education and convenient interface for dataset
...The "mature" data is in json sequence format and can be modeled by XKT and TKT(TBA) The analysis dataset tool only supports the json sequence format. To check the following statical indexes of the dataset. In order to better verify the effectiveness of the model, the dataset is usually divided into train/valid/test or using kfold method. Each item in the sequence represents one interaction. The first element of the item is the exercise id (in some works, the exercise id is not one-to-one mapped to one knowledge unit(ku)/concept, but in junyi, one exercise contains one ku) and the second one indicates whether the learner correctly answers the exercise, 0 for wrongly while 1 for correctly. -
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ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation. Covers key aspects of data sourcing, financial feature engineering, and portfolio management. The design and evaluation of long-short strategies based on a broad range of ML algorithms, how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news. ... -
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3 Rs of Software Architecture
A guide on how to write readable, reusable, and refactorable software
This guide aims to help software developers think more clearly about how to build systems that are not only functional today but maintainable into the future. It focuses on three architectural “ilities”: readability, reusability, and refactorability, presenting them in a hierarchical framework so developers can evaluate and improve their code and system design. The project uses a simple shopping-cart application written in JavaScript and React/Redux to illustrate how code evolves from “bad”... -
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wav2letter++
Facebook AI research's automatic speech recognition toolkit
First, install Flashlight (using the 0.3 branch is required) with the ASR application. This repository includes recipes to reproduce the following research papers as well as pre-trained models. All results reproduction must use Flashlight <= 0.3.2 for exact reproducibility. At least one of LZMA, BZip2, or Z is required for LM compression with KenLM. It is highly recommended to build KenLM with position-independent code (-fPIC) enabled, to enable python compatibility. After installing, run... -
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PyTorch GAN Zoo
A mix of GAN implementations including progressive growing
...The project provides modular implementations of popular GAN architectures, including Progressive Growing of GANs (PGAN), DCGAN, and an experimental StyleGAN version. It is built to support both researchers and developers who want to train, evaluate, and extend GANs efficiently across diverse datasets such as CelebA-HQ, FashionGen, DTD, and CIFAR-10. In addition to core GAN training, the repository includes tools for model evaluation, such as Inception Score and SWD metrics, as well as advanced features like GDPP for diverse generation and AC-GAN conditioning for class-specific synthesis. ... -
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jieba
Stuttering Chinese word segmentation
...The search engine mode, on the basis of the precise mode, divides the long words again to improve the recall rate, which is suitable for word segmentation in search engines. The paddle mode uses the PaddlePaddle deep learning framework to train the sequence labeling (bidirectional GRU) network model to achieve word segmentation. Also supports part-of-speech tagging. To use paddle mode, you need to install paddlepaddle-tiny, pip install paddlepaddle-tiny==1.6.1. Currently paddle mode supports jieba v0.40 and above. For versions below jieba v0.40, please upgrade jieba, pip install jieba --upgrade. -
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Deep Learning for Medical Applications
Deep Learning Papers on Medical Image Analysis
Deep-Learning-for-Medical-Applications is a repository that compiles deep learning methods, code implementations, and examples applied to medical imaging and healthcare data. The project addresses domain-specific challenges like segmentation, classification, detection, and multimodal data (e.g. MRI, CT, X-ray) using state-of-the-art architectures (e.g. U-Net, ResNet, GAN variants) tailored to medical constraints (small datasets, annotation costs, class imbalance). It includes Jupyter... -
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JCSprout
Basic, concurrent algorithm
JCSprout is a curated learning path for Java engineers that mixes concise notes, diagrams, and runnable examples to cover core computer science and JVM topics. It walks readers through data structures and algorithms, networking fundamentals, Java concurrency, JVM memory model and GC, and common interview problem patterns. The repository emphasizes understanding over memorization, linking conceptual summaries with small code artifacts that can be compiled and profiled. It also highlights best practices around collections, thread safety, lock-free techniques, and performance trade-offs so learners can make informed design decisions. ... -
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Bell-412
Highly realistic and immersive model for Flightgear.
This is the Textron Bell412 helicopter for the Flightgear flight simulator. This rotorcraft is designed in order to provide the most realistic flight experience to player, including design and behaviour. This project is updated every 1 to 6 months with major improvments. Keep updated and happy Flightgear. My Youtube Channel: https://www.youtube.com/channel/UCmxlWKtFn19wYVe2DYU9XCw/videos v0.9.1- r54 released Feb/2018 : * FDM: updated. Better IT. * MODEL: new texture & model updated * BOMBABLE: WIP (weapons ready) * MISC: 3rd Party COPYRIGHT Included and 3rd party data clearly detailed. ===> see CHANGELOG. -
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Learning to Learn in TensorFlow
Learning to Learn in TensorFlow
...The repository provides code for training and evaluating learned optimizers that can generalize across different problem types, such as quadratic functions and image classification tasks (MNIST and CIFAR-10). Using TensorFlow, it defines a meta-optimizer model that learns by observing and adapting to the optimization trajectories of other models. The project allows users to compare performance between traditional optimizers and the learned optimizer (L2L) on various benchmarks, demonstrating how optimization strategies can be learned through experience. The design supports both single-variable and high-dimensional problems, and includes tools for evaluating how well a learned optimizer performs on unseen tasks. -
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MRiLab
A Numerical MRI Simulation Platform
...If you find MRiLab useful for your work, please cite this paper: Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model. IEEE Transactions on Medical Imaging. 2016. doi: 10.1109/TMI.2016.2620961 -
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JMCAD is an program for the modeling and simulation of complex dynamic systems. This includes the ability to construct and simulate block diagrams. The visual block diagram interface offers a simple method for constructing, modifying and maintaining complex system models. The simulation engine provides fast and accurate solutions for linear, nonlinear, continuous time, discrete time, time varying and hybrid system designs. With JMCAD, users can quickly develop software or "virtual"...
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Blood Bank
A BloodBank Administration Software
This networked(LAN) software is made as an IT solution for a blood bank to support the day-to-day actions. The software is coded using Java language and the UI is implemented with JavaFX. Please concern that this software is developed mainly for educational purpose.
