Search Results for "raspberry-gpio-python" - Page 12
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Showing 672 open source projects for "raspberry-gpio-python"
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Key-book
Proofs, cases, concept supplements, and reference explanations
The book "Introduction to Machine Learning Theory" (hereinafter referred to as "Introduction") written by Zhou Zhihua, Wang Wei, Gao Wei, and other teachers fills the regret of the lack of introductory works on machine learning theory in China. This book attempts to provide an introductory guide for readers interested in learning machine learning theory and researching machine learning theory in an easy-to-understand language. "Guide" mainly covers seven parts, corresponding to seven... -
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DeepXDE
A library for scientific machine learning & physics-informed learning
DeepXDE is a library for scientific machine learning and physics-informed learning. DeepXDE includes the following algorithms. Physics-informed neural network (PINN). Solving different problems. Solving forward/inverse ordinary/partial differential equations (ODEs/PDEs) [SIAM Rev.] Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving... -
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. We also feature a deep... -
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openTSNE
Extensible, parallel implementations of t-SNE
openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1], a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2], massive speed improvements [3] [4] [5], enabling t-SNE to scale to millions of data points, and various tricks to improve the global alignment of the resulting visualizations. -
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cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio
Cracking the Data Science Interview is an open educational repository that collects study materials, resources, and reference links for preparing for data science interviews. The project organizes content across many fundamental areas of data science, including statistics, probability, SQL, machine learning, and deep learning. It includes cheat sheets that summarize important technical concepts commonly discussed during technical interviews. The repository also provides links to recommended... -
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python-small-examples
Focus on creating classic Python small examples and cases
python-small-examples is an open-source educational repository that contains hundreds of concise Python programming examples designed to illustrate practical coding techniques. The project focuses on teaching programming concepts through small, focused scripts that demonstrate common tasks in data processing, visualization, and general programming. -
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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
DeepPavlov makes it easy for beginners and experts to create dialogue systems. The best place to start is with user-friendly tutorials. They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual... -
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Datapipe
Real-time, incremental ETL library for ML with record-level depend
Datapipe is a real-time, incremental ETL library for Python with record-level dependency tracking. Datapipe is designed to streamline the creation of data processing pipelines. It excels in scenarios where data is continuously changing, requiring pipelines to adapt and process only the modified data efficiently. This library tracks dependencies for each record in the pipeline, ensuring minimal and efficient data processing. -
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seq2seq-couplet
Play couplet with seq2seq model
seq2seq-couplet is a deep learning application that generates Chinese couplet responses using a sequence-to-sequence model built with TensorFlow. Its purpose is not general machine translation, but a specialized text generation task in which the model produces a matching second line for a given first line in the style of traditional couplets. The repository includes the code needed to train the model, configure file paths and hyperparameters, and evaluate progress through loss and BLEU score... -
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pomegranate
Fast, flexible and easy to use probabilistic modelling in Python
pomegranate is a library for probabilistic modeling defined by its modular implementation and treatment of all models as the probability distributions they are. The modular implementation allows one to easily drop normal distributions into a mixture model to create a Gaussian mixture model just as easily as dropping a gamma and a Poisson distribution into a mixture model to create a heterogeneous mixture. But that's not all! Because each model is treated as a probability distribution,... -
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Bootstrap Your Own Latent (BYOL)
Usable Implementation of "Bootstrap Your Own Latent" self-supervised
Practical implementation of an astoundingly simple method for self-supervised learning that achieves a new state-of-the-art (surpassing SimCLR) without contrastive learning and having to designate negative pairs. This repository offers a module that one can easily wrap any image-based neural network (residual network, discriminator, policy network) to immediately start benefitting from unlabelled image data. There is now new evidence that batch normalization is key to making this technique... -
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The Hundred-Page Machine Learning Book
The Python code to reproduce illustrations from Machine Learning Book
The Hundred-Page Machine Learning Book is the official companion repository for The Hundred-Page Machine Learning Book written by machine learning researcher Andriy Burkov. The repository contains Python code used to generate the figures, visualizations, and illustrative examples presented in the book. Its purpose is to help readers better understand the concepts explained in the text by allowing them to run and experiment with the underlying code themselves. The book itself provides a concise overview of machine learning theory and practice, covering topics such as supervised learning, unsupervised learning, neural networks, and optimization algorithms. ... -
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SMILI
Scientific Visualisation Made Easy
The Simple Medical Imaging Library Interface (SMILI), pronounced 'smilie', is an open-source, light-weight and easy-to-use medical imaging viewer and library for all major operating systems. The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with... -
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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. -
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dashAI
dashAI: an interactive platform for training, evaluating and deploying
dashAI is an open-source, No-code workbench for Exploratory Data Analysis and classical ML. Visual data preparation, multi-model experiments, XAI explainability, and a plugin-based extensible catalog. The platform guides users through a complete, traceable workflow — data ingestion → visual exploration → preprocessing → model training → evaluation → explainability — without writing a single line of code. Each step is explicit and reversible, keeping the user in control rather than... -
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GNNPCSAFT
Smart Thermodynamic Modeling with Graph Neural Networks
The GNNPCSAFT app is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. To install the GNNPCSAFT app, download the... -
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Uranie
Uranie is CEA's uncertainty analysis platform, based on ROOT
...It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. Is is available both for Unix and Windows platforms (a dedicated platform archive is available on request). Note : if you have downloaded version 3.12 before the 8th of february, a patch exists for a minor bug on TOutputFileKey file, don't hesitate to ask us. -
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GNNPCSAFT Web App
Smart Thermodynamic Modeling with Graph Neural Networks
The GNNPCSAFT Web App is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. - CPU-compatible desktop build The standard... -
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BitNet
BitNet: Scaling 1-bit Transformers for Large Language Models
BitNet is a machine learning research implementation that explores extremely low-precision neural network architectures designed to dramatically reduce the computational cost of large language models. The project implements the BitNet architecture described in research on scaling transformer models using extremely low-bit quantization techniques. In this approach, neural network weights are quantized to approximately one bit per parameter, allowing models to operate with far lower memory... -
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imgclsmob Deep learning networks
Sandbox for training deep learning networks
imgclsmob is a deep learning research repository focused on implementing and experimenting with convolutional neural networks for computer vision tasks. The project serves as a sandbox for training and evaluating a wide variety of neural network architectures used in image analysis. It includes implementations of models used for tasks such as image classification, object detection, semantic segmentation, and pose estimation. The repository also contains scripts that help train models,... -
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Zylthra
Zylthra: A PyQt6 app to generate synthetic datasets with DataLLM.
Welcome to Zylthra, a powerful Python-based desktop application built with PyQt6, designed to generate synthetic datasets using the DataLLM API from data.mostly.ai. This tool allows users to create custom datasets by defining columns, configuring generation parameters, and saving setups for reuse, all within a sleek, dark-themed interface. -
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PoseidonQ - AI/ML Based QSAR Modeling
ML based QSAR Modelling And Translation of Model to Deployable WebApps
- This Software was made with an intention to make QSAR/QSPR development more efficient and reproducible. - Published in ACS, Journal of Chemical Information and Modeling . Link : https://pubs.acs.org/doi/10.1021/acs.jcim.4c02372 - Simple to use and no compromise on essential features necessary to make reliable QSAR models. - From Generating Reliable ML Based QSAR Models to Developing Your Own QSAR WebApp. For any feedback or queries, contact kabeermuzammil614@gmail.com - Available on... -
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Loss
The Brutalist Market Analyzer
...Instead of a monolithic architecture, LOSS uses a Split-Stack approach: A Vercel Relay handles raw data fetching, bypassing CORS and rate limits. A Render Python Backend acts as the "Brain," executing heavy machine learning and NLP tasks. Try the live application in action here: https://loss-by-vignesh.vercel.app before downloading the source code Visit For More Details : https://github.com/Vignesh-72/Loss -
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Conscious Artificial Intelligence
It's possible for machines to become self-aware.
...A number of prototypes will be developed as the project progresses. This project has 2 subprojects: Object Pascal based CAI NEURAL API - https://github.com/joaopauloschuler/neural-api Python based K-CAI NEURAL API - https://github.com/joaopauloschuler/k-neural-api A video from the first prototype has been made: http://www.youtube.com/watch?v=qH-IQgYy9zg Above video shows a popperian agent collecting mining ore from 3 mining sites and bringing to the base. At the time the agent is born, it doesn't know how to walk nor it knows that it feels pleasure by mining. ...
