Search Results for "no code" - Page 54
Sort By:
Showing 2682 open source projects for "no code"
View related business solutions-
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
Build Data Resilience - Take the Assessment TodayIs your recovery strategy as strong as you think? Take this quick self-assessment to check your recovery readiness and gain tailored insights. In only 2 minutes, you'll learn where you fall on the recovery readiness scale.
-
1
G-CPS
Lite CPS Tester
G-CPS (Golden CPS) It is a Very Lite program to measure your CPS G-CPS is a very lightweight program, it contains multiple color themes, and several options to adjust the test time (1, 3, 5, 10, 15, 30 and 60) s. It also saves the CPS record that you have achieved, each time mode has a different record. Source Code: GitHub: https://github.com/PETEROLO291/G-CPS You can try it out with: https://sourceforge.net/projects/flame-auto-clicker/ Info: Installer: 9.3MB Program: 27.5MB Version: 2.1.1 - I am totally open to feature requests for future updates -
2
Blankly
Easily build, backtest and deploy your algo in just a few lines
...We make it super easy for you so you can focus on building better trading algos. Your models can run on any platform, and on any supported exchange. We make that as easy as just changing one line of code. -
3
TheoDORE
Theoretical Density, Orbital Relaxation and Exciton analysis
...TheoDORE is interfaced to a number of quantum chemical programs (Q-Chem, Columbus, Turbomole, ...) and can be used for a wide range of excited state methods (MR-CI, ADC, CC, TDDFT). It is written in a modular fashion to allow for an easy extension of these capabilities. Code development moved to github: https://github.com/felixplasser/theodore-qc -
4
Discord ATIO
ATIO is a AIO Script Developed with Python3
...Its safest use case is defensive review, malware-awareness training, or historical analysis of common Discord abuse tooling. The project is best categorized as high-risk archived code rather than software for practical deployment. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
5
pspider
Simple Python framework for building multithreaded web crawlers
PSpider is a lightweight web crawling framework written in Python designed to simplify the development of custom web spiders. It focuses on providing an easy-to-understand architecture while still supporting concurrent crawling for improved performance. It uses a multithreaded model that separates the crawling workflow into several components responsible for fetching, parsing, and saving data. Tasks are managed through queues, allowing different parts of the crawler to process work... -
6
pyTorch Tutorials
Build your neural network easy and fast
pyTorch Tutorials is an open-source collection of hands-on tutorials designed to teach developers how to build neural networks with the PyTorch framework. It covers the fundamentals of PyTorch from basic tensor operations to constructing full neural network models, making it suitable for beginners and intermediate learners alike. The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks,... -
7
Quintenzirkel
Circle of Fifths for musicians
Quintenzirkel (C) 2022 Alexander Groß The present program is subject to the GNU GPL version 3, which is included in the file COPYING the source code. I use the international notation in the program, so german H = B, German B = A#, also I don't use flats, but use the respective sharp variant, i.e. Bb = A#. The application is written in Python and uses Kivy (https://kivy.org) as GUI library, but no other than the standard Python libs. -
8
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... -
9
PC-BASIC - a GW-BASIC emulator
A free, cross-platform emulator for GW-BASIC, PCjr & Tandy BASIC
.... -- This page is an archive and hosts the legacy version 1.2 of PC-BASIC only. -- For documentation and the latest releases, please see the official homepage: www.pc-basic.org Four source code, discussions and to report bugs please see the GitHub project page https://github.com/robhagemans/pcbasic -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
10
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+. ... -
11
m2cgen
Transform ML models into a native code
m2cgen (Model 2 Code Generator) - is a lightweight library that provides an easy way to transpile trained statistical models into a native code (Python, C, Java, Go, JavaScript, Visual Basic, C#, PowerShell, R, PHP, Dart, Haskell, Ruby, F#, Rust, Elixir). Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies. -
12
TensorFlowOnSpark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters
By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. -
13
DeeProtGO
DeeProtGO is a deep learning model for predicting GO terms of proteins
This project contains the source code of DeeProtGO as well as an example of its use when predicting GO terms of the biological process sub-ontology for eukaryotic proteins. -
14
Google Cloud Vision API examples
Sample code for Google Cloud Vision
The cloud-vision repository is a sample code collection for the Google Cloud Vision API that shows developers how to implement image analysis tasks across a wide range of languages and platforms. It contains examples organized by language and environment, including Go, Java, Node.js, PHP, Python, Ruby, .NET, Android, iOS, and even a Chrome extension, which makes it especially valuable as a cross-platform learning resource. -
15
Unet
Source code for unet-pytorch, which can train its own model
Unet-pytorch is a PyTorch implementation of U-Net for semantic segmentation workflows. The repository is built around training, prediction, and mIoU evaluation for VOC-style segmentation data and medical-style datasets. It includes scripts for general training, medical dataset training, prediction, annotation handling, model summaries, and evaluation. The project supports multiple backbones, data processing utilities, extensive comments, and adjustable training parameters. Its README notes... -
16
LayoutParser
A Unified Toolkit for Deep Learning Based Document Image Analysis
With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process. A complete instruction for installing the main Layout Parser library and auxiliary components. Learn how to load DL Layout models and use them for layout detection. The full list of layout models currently available in Layout Parser. ... -
17
RQ-Transformer
Implementation of RQ Transformer, autoregressive image generation
...A short sequence length is important for an AR model to reduce its computational costs to consider long-range interactions of codes. However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off. -
18
Jupyter Dash
Dash v2.11+ has Jupyter support built in
Dash 2.11 and later supports running Dash apps in classic Jupyter Notebooks and in JupyterLab without the need to update the code or use the additional JupyterDash library. If you are using an earlier version of Dash, you can run Dash apps in a notebook using JupyterDash. This page documents additional options available when running Dash apps in notebooks as well as troubleshooting information. -
19
EasyNLP
EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit
...It is seamlessly integrated to Platform of AI (PAI) products, including PAI-DSW for development, PAI-DLC for cloud-native training, PAI-EAS for serving, and PAI-Designer for zero-code model training. -
20
The Pythonizer
Converts a Java program to the equivalent Python program
An automatic source-code converter (transcompiler) that converts a valid Java program to the equivalent Python program. Uses Python naming conventions and indentation convention. -
21
bert4keras
Keras implement of transformers for humans
...A cleaner, lighter version of bert for keras. This is the keras version of the transformer model library re-implemented by the author and is committed to combining transformer and keras with as clean code as possible. The original intention of this project is for the convenience of modification and customization, so it may be updated frequently. Load the pre-trained weights of bert/roberta/albert for fine-tune. Implement the attention mask required by the language model and seq2seq. Pre-training code from zero (supports TPU, multi-GPU, please see pertaining). ... -
22
OhMyForm
Free open source alternative to TypeForm, TellForm, or Google Forms
...With OhMyForm you'll get all the features you need to run, administer, analyze and distribute forms for free. With our opensource community, OhMyForm is always improving and is vetted by top programming experts around the world, ensuring that our code is top-notch, eliminating potential bugs and errors. -
23
...Characters are not lost even on non-HiDPI (non-Retina) Windows. Consider readability of special characters such as subscripts and superscripts ( assuming JuliaLang ). The font file is SIL Open Font License 1.1. The source code of the synthesis script etc. is MIT. The Pomicons glyphs included in Nerd Fonts are excluded from the generator because they are licensed for commercial use.
-
24
GLIDE (Text2Im)
GLIDE: a diffusion-based text-conditional image synthesis model
...It demonstrates how diffusion-based generative models can be conditioned on text to produce highly detailed and coherent visual outputs. The repository provides both model code and pretrained checkpoints, making it possible for researchers and developers to experiment with text-to-image synthesis. GLIDE includes advanced techniques such as classifier-free guidance, which improves the quality and alignment of generated images with the input text. The project also offers sampling scripts and utilities for exploring how diffusion models can be applied to multimodal tasks. ... -
25
YOLOv3
Object detection architectures and models pretrained on the COCO data
...Export and deploy your YOLOv5 model with just 1 line of code. There are also loads of quickstart guides and tutorials available to get your model where it needs to be. Create state of the art deep learning models with YOLOv5
