Search Results for "g-code" - Page 52
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Showing 1797 open source projects for "g-code"
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Codeball AI
AI Code Review that finds bugs and fast-tracks your code
Codeball is a code review AI that scores pull requests on a grade from 0 (needs careful review) to 1. Use Codeball to add labels to help you focus, auto-approve PRs, and more. The Codeball action is easy to use (sane defaults) and is highly customizable to fit your workflow when needed. Label PRs when you should review them with caution. Stay sharp, don't let the bugs pass through. -
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Python ML Jupyter Notebooks
Practice and tutorial-style notebooks
...The project provides numerous examples and tutorials covering classical machine learning techniques such as regression, classification, clustering, and dimensionality reduction. It includes code implementations that show how to build models using popular libraries like scikit-learn, NumPy, pandas, and Matplotlib. The repository is designed to help learners understand both the theory and practical implementation of machine learning algorithms through step-by-step code examples. Many notebooks include explanations of algorithm behavior, data preparation techniques, and evaluation methods for machine learning models. ... -
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Termin-Bot
Telegram bot to get notifications about free appointments
...Sometimes it is tough to get a termin in public institutions and this bot is a way to automate termin checks and send Telegram notifications, if free termin was found. The code could be also considered as a template for subsequent development. The principle of bot operation is to refresh a webpage with needed appointments periodically [TERMIN_URL] and to recognize the change on it. E.g., the page always contains the phrase "No free appointments" [NO_TERMIN_FOUND_STRING]. Then some free appointments are added to the system, and the phrase "No free appointments" disappears. ... -
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Python Machine Learning 3rd Ed.
The "Python Machine Learning (3rd edition)" book code repository
Python Machine Learning 3rd Ed. repository contains the complete source code that accompanies the book Python Machine Learning by Sebastian Raschka and collaborators. The project provides implementations of machine learning algorithms and data science workflows described in the book, enabling readers to experiment with real code while studying theoretical concepts. The repository includes Python notebooks and scripts demonstrating techniques such as data preprocessing, classification, regression, clustering, neural networks, and model evaluation. ... -
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AirSim
A simulator for drones, cars and more, built on Unreal Engine
AirSim is an open-source, cross platform simulator for drones, cars and more vehicles, built on Unreal Engine with an experimental Unity release in the works. It supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim's development is oriented towards the goal of creating a... -
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Hello AI World
Guide to deploying deep-learning inference networks
...In just a couple of hours, you can have a set of deep learning inference demos up and running for realtime image classification and object detection on your Jetson Developer Kit with JetPack SDK and NVIDIA TensorRT. 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. ... -
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BioNLP is an initiative by the University of Colorado Denver Health Sciences Center to create and distribute code, software, and data for applying natural language processing techniques to biomedical texts
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Pattern Recognition and Machine Learning
Repository of notes, code and notebooks in Python
...The project recreates many of the mathematical concepts and diagrams from the book using executable Jupyter notebooks, allowing readers to experiment directly with the algorithms described in the text. Each section of the repository corresponds to chapters in the book and includes code examples that demonstrate statistical modeling, machine learning methods, and Bayesian inference techniques. These notebooks provide visualizations and computational demonstrations that help clarify complex topics such as probabilistic models, neural networks, kernel methods, and graphical models. The repository also includes implementations of sampling methods, clustering algorithms, and dimensionality reduction techniques used throughout machine learning research. -
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PyTorch Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, etc.
TLlib is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms or readily apply existing algorithms. We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Guided Diffusion
Codebase for Diffusion Models Beat GANS on Image Synthesis
...It is derived from OpenAI’s improved-diffusion work, enhanced to include guided generation where a classifier (or other guidance mechanism) can steer sampling toward desired classes or attributes. The code provides model definitions (UNet, diffusion schedules), sampling and training scripts, and utilities for guidance and evaluation. A key insight is that combining diffusion sampling with classifier gradients allows fine control over the generated images, trading off diversity vs fidelity. The repository includes scripts such as image_train.py, image_sample.py, and classifier_train.py to train diffusion models, generate samples, and train guiding classifiers. ... -
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StudioGAN
StudioGAN is a Pytorch library providing implementations of networks
...Moreover, StudioGAN provides an unprecedented-scale benchmark for generative models. The benchmark includes results from GANs (BigGAN-Deep, StyleGAN-XL), auto-regressive models (MaskGIT, RQ-Transformer), and Diffusion models (LSGM++, CLD-SGM, ADM-G-U). StudioGAN is a self-contained library that provides 7 GAN architectures, 9 conditioning methods, 4 adversarial losses, 13 regularization modules, 6 augmentation modules, 8 evaluation metrics, and 5 evaluation backbones. Among these configurations, we formulate 30 GANs as representatives. Each modularized option is managed through a configuration system that works through a YAML file. -
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Video Pre-Training
Learning to Act by Watching Unlabeled Online Videos
The Video PreTraining (VPT) repository provides code and model artifacts for a project where agents learn to act by watching human gameplay videos—specifically, gameplay of Minecraft—using behavioral cloning. The idea is to learn general priors of control from large-scale, unlabeled video data, and then optionally fine-tune those priors for more goal-directed behavior via environment interaction. -
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ConvNeXt
Code release for ConvNeXt model
ConvNeXt is a modernized convolutional neural network (CNN) architecture designed to rival Vision Transformers (ViTs) in accuracy and scalability while retaining the simplicity and efficiency of CNNs. It revisits classic ResNet-style backbones through the lens of transformer design trends—large kernel sizes, inverted bottlenecks, layer normalization, and GELU activations—to bridge the performance gap between convolutions and attention-based models. ConvNeXt’s clean, hierarchical structure... -
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ISLR-python
An Introduction to Statistical Learning
...The datasets used in the book are also included so that users can run experiments directly within the provided notebooks. By translating the statistical learning material into Python code, the repository makes the book’s concepts accessible to a wider community of Python users. -
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flutter_ume
UME is an in-app debug kits platform for Flutter
...It lets developers embed a suite of debugging tools directly into a Flutter app (during development or debug builds), enabling inspection, performance monitoring, UI debugging, network request inspection, widget hierarchy introspection, and more — all from within the running app. UME bundles multiple “plugin kits” (e.g., UI inspector, performance monitor, device info panel, network inspector via Dio, console log viewer, code viewer, touch indicator) so developers can enable only the tools they need. It supports Flutter 3 and Dart with null-safety, integrates as a dev dependency (so it doesn’t ship by default with production builds), and offers a simple API to activate the debug overlay. Because it's open source under MIT license, developers can also create custom plugin kits and extend UME’s capabilities to suit their debugging or testing workflow. -
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nlpaug
Data augmentation for NLP
This Python library helps you with augmenting nlp for your machine learning projects. Visit this introduction to understand Data Augmentation in NLP. Augmenter is the basic element of augmentation while Flow is a pipeline to orchestra multi augmenters together. -
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Alphafold2
Unofficial Pytorch implementation / replication of Alphafold2
...Once this is replicated, I intend to fold all available amino acid sequences out there in-silico and release it as an academic torrent, to further science. Deepmind has open sourced the official code in Jax, along with the weights! This repository will now be geared towards a straight pytorch translation with some improvements on positional encoding. lhatsk has reported training a modified trunk of this repository, using the same setup as trRosetta, with competitive results. The underlying assumption is that the trunk works on the residue level, and then constitutes to atomic level for the structure module, whether it be SE3 Transformers, E(n)-Transformer, or EGNN doing the refinement. -
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Machine Learning Glossary
Machine learning glossary
...The content is organized into sections that progressively introduce key ideas from basic machine learning concepts to more advanced mathematical topics. Many pages include diagrams or code examples to illustrate how algorithms work in practice. Because the project emphasizes accessibility, it is particularly useful for beginners who want a conceptual overview of machine learning terminology before diving into more technical research papers. -
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WaveRNN
WaveRNN Vocoder + TTS
...As a vocoder, WaveRNN models raw audio with a compact recurrent neural network that can generate high-quality waveforms more efficiently than many traditional autoregressive models. The repository includes scripts and code for preprocessing datasets such as LJSpeech, training Tacotron to produce mel spectrograms, training WaveRNN on those spectrograms (with optional GTA data), and finally generating audio. A quick_start.py script allows users to immediately synthesize example sentences from a pretrained model and inspect both generated audio and attention plots. ... -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the... -
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AI Atelier
Based on the Disco Diffusion, version of the AI art creation software
Based on the Disco Diffusion, we have developed a Chinese & English version of the AI art creation software "AI Atelier". We offer both Text-To-Image models (Disco Diffusion and VQGAN+CLIP) and Text-To-Text (GPT-J-6B and GPT-NEOX-20B) as options. Making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. When a modified version is used to provide a service over a network, the complete source code of the modified version must be made available. Create 2D and 3D animations and not only still frames (from Disco Diffusion v5 and VQGAN Animations). ... -
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tf2_course
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course
...The material is intended for learners who already have foundational knowledge of ML and wish to deepen their understanding of deep learning frameworks and practices. The repo supports experimentation: you can run code, tweak hyperparameters, and follow guided exercises that strengthen practical mastery. Rather than being book-based, it is course-based, meaning the flow, examples and structure lean toward interactive teaching and incremental builds. It’s well-suited for those who want a focused, deep-learning path rather than a broad ML textbook. -
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pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
...See also probml-utils for some utility code that is shared across multiple notebooks. -
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Mask2Former
Code release for "Masked-attention Mask Transformer
Mask2Former is a unified segmentation architecture that handles semantic, instance, and panoptic segmentation with one model and one training recipe. Its core idea is to cast segmentation as mask classification: a transformer decoder predicts a set of mask queries, each with an associated class score, eliminating the need for task-specific heads. A pixel decoder fuses multi-scale features and feeds masked attention in the transformer so each query focuses computation on its current spatial... -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. 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+. ...
