Search Results for "artificial intelligence python" - Page 71
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3403 projects for "artificial intelligence python" with 1 filter applied:
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
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Fashion-MNIST
A MNIST-like fashion product database
Fashion-MNIST is an open-source dataset created by Zalando Research that provides a standardized benchmark for image classification algorithms in machine learning. The dataset contains grayscale images of fashion products such as shirts, shoes, coats, and bags, each labeled according to its clothing category. It was designed as a direct replacement for the original MNIST handwritten digits dataset, maintaining the same structure and image size so that researchers could easily switch datasets... -
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Piano transcription
Task of transcribing piano recordings into MIDI files
Piano transcription is an open-source high-resolution piano transcription system by ByteDance that converts raw audio recordings of piano performance into symbolic MIDI files — detecting note onsets, offsets, pitch, velocity, and even pedal usage. The system is implemented in Python (PyTorch) and is capable of accurate transcription of polyphonic piano recordings, even with complex passages and pedal techniques, making it suitable for classical piano music. By using this transcription tool,... -
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PyTorch Handbook
The pytorch handbook is an open source book
PyTorch Handbook is an open-source educational project designed to help developers and researchers quickly learn deep learning using the PyTorch framework. The repository functions as an online handbook that explains how to build, train, and evaluate neural network models using PyTorch. It includes tutorials and examples that demonstrate common deep learning tasks such as image classification, neural network design, model training workflows, and evaluation techniques. The material is written... -
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Acme
A library of reinforcement learning components and agents
Acme is a framework from DeepMind for building scalable and reproducible reinforcement learning agents. It emphasizes modular components, distributed training, and ease of experimentation. -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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MeshCNN in PyTorch
Convolutional Neural Network for 3D meshes in PyTorch
MeshCNN is a deep learning framework designed specifically for processing 3D triangular mesh data using convolutional neural networks. Unlike traditional CNNs that operate on images or voxel grids, MeshCNN performs convolution operations directly on the edges of mesh structures. This design allows the model to capture geometric relationships between mesh elements while preserving the underlying topology of 3D shapes. The framework introduces specialized layers such as edge-based convolution,... -
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The goal of this project is to investigate optimal ways to do genre classification for the ten indigenous South African languages. Funded by Dept of Arts and Culture of the SA Government. http://www.trifonius.co.za/projects/genre-classification
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Deeplearning.ai
Study notes, summaries, and auxiliary materials for deep learning
Deeplearning.ai collects study notes, summaries, and auxiliary materials aligned with the popular deep learning course series many learners take early in their AI journey. It distills core ideas such as optimization, regularization, convolutional networks, sequence models, and practical training tricks. The explanations aim to bridge theory and practice, often connecting mathematical intuition to code-level implications. By organizing the content as “books” or structured notes, it gives... -
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CodeSearchNet
Datasets, tools, and benchmarks for representation learning of code
CodeSearchNet is a large-scale dataset and research benchmark designed to advance the development of systems that retrieve source code using natural language queries. The project was created through collaboration between GitHub and Microsoft Research and aims to support research on semantic code search and program understanding. The dataset contains millions of pairs of source code functions and corresponding documentation comments extracted from open-source repositories. These pairs allow... -
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Read Aloud
An awesome browser extension that reads aloud webpage content
Read Aloud is a browser extension for Chrome, Firefox, and other Chromium-based browsers that converts webpage text to audio using text-to-speech technology. It is designed to work on a wide variety of sites, including news, blogs, online textbooks, course materials, fanfiction, and more. The extension targets users who prefer listening over reading, as well as people with dyslexia, other learning disabilities, or eye strain, and children learning to read. Read Aloud lets users choose from... -
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.
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Deep Learning Papers Reading Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn
Deep Learning Papers Reading Roadmap is a widely known curated reading plan for deep learning that helps newcomers and practitioners navigate the vast literature in a structured and intentional way. It is built around several guiding principles: moving from outline to detail, from older foundational papers to state-of-the-art work, and from generic to more specialized areas while keeping a focus on impactful contributions. The roadmap organizes papers into categories such as fundamentals,... -
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Guia do Cientista de Dados das Galáxias
Repository for gathering information on study materials
Guia do Cientista de Dados das Galáxias is an open-source community repository that aggregates educational resources, tools, and references related to data science, machine learning, and analytics. The project was created by the Pizza de Dados community with the goal of organizing useful materials for people interested in learning or working in the data science ecosystem. The repository collects links to books, podcasts, tutorials, datasets, communities, and study groups that can help... -
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Yoha
A practical hand tracking engine
Yoha is a browser-based hand tracking engine designed to enable real-time gesture recognition and interaction using standard webcams, making it accessible for web applications without specialized hardware. Built using JavaScript and TensorFlow.js, it runs directly in the browser and performs inference on-device, eliminating the need for server-side processing. The engine is capable of detecting 21 two-dimensional hand landmarks, allowing developers to build applications that respond to... -
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AhoTTS - TTS for Basque and Spanish
Text-to-Speech for Basque and Spanish
Text-to-Speech conversor for Basque and Spanish. It includes linguistic processing and built voices for the languages aforementioned. Its acoustic engine is based on hts_engine and it uses a high quality vocoder called AhoCoder. Developed by Aholab Signal Processing Laboratory: https://aholab.ehu.es/aholab/ http://aholab.ehu.es/ahocoder/ -
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godlp
Sensitive information protection toolkit
godlp appears to be another software project from ByteDance — however, as of the most recent checks, there’s very little publicly available information about it: the repository exists under ByteDance’s GitHub, but its documentation, README, and metadata are minimal (or not human-readable), and the project seems to have limited community visibility compared to their other major tools. Because of that opacity, one must infer that godlp is likely a specialized internal or early-stage tool,... -
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text_renderer
Generate text images for training deep learning ocr model
Text Renderer is a Python utility for generating synthetic text images used to train optical character recognition models. It supports Latin and non-Latin writing systems and produces matching labels alongside the generated images. Users can supply their own corpora, fonts, character sets, backgrounds, and YAML configuration files. Available effects include perspective distortion, random cropping, curved text, borders, variable spacing, lines, embossing, color changes, inversion, and blur. A... -
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DeepDanbooru
AI based multi-label girl image classification system
DeepDanbooru is a deep learning system designed to automatically tag anime-style images using neural networks trained on datasets derived from the Danbooru imageboard. The project focuses on multi-label image classification, where a model predicts multiple descriptive tags that represent visual elements in an image. These tags may include characters, styles, clothing, emotions, or other attributes associated with anime artwork. The system uses convolutional neural networks trained on large... -
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This project aims to promote the MultiAgent Programming (MAP) approach by providing a suitable integration of tools and languages for programming the following dimensions: agents (Jason), environment (Cartago), and organisation (Moise).
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GiantMIDI-Piano
Classical piano MIDI dataset
GiantMIDI-Piano is a large-scale symbolic classical piano music dataset built by applying the piano_transcription system on a vast collection of piano performance recordings. The dataset contains thousands of piano works, spanning a large number of composers and styles, with each piece transcribed into high-precision MIDI files capturing note events, pedal usage, velocities, etc. It provides a resource for music information retrieval (MIR), symbolic music modeling, composer classification,... -
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Following Instructions with Feedback
Training Language Models to Follow Instructions with Human Feedback
The following-instructions-human-feedback repository contains the code and supplementary materials underpinning OpenAI’s work in training language models (InstructGPT models) that better follow user instructions through human feedback. The repo hosts the model card, sample automatic evaluation outputs, and labeling guidelines used in the process. It is explicitly tied to the “Training language models to follow instructions with human feedback” paper, and serves as a reference for how OpenAI... -
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email2phonenumber
OSINT tool to discover phone numbers using an email address
email2phonenumber is an open source OSINT (Open Source Intelligence) tool designed to help researchers identify a target’s phone number using only an email address. The project was created as a proof-of-concept during research into new OSINT methodologies for extracting personal information from publicly accessible sources and account recovery mechanisms. The tool works by automating interactions with password reset processes on various online services, which may reveal masked phone number... -
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Music Source Separation
Separate audio recordings into individual sources
Music Source Separation is a PyTorch-based open-source implementation for the task of separating a music (or audio) recording into its constituent sources — for example isolating vocals, instruments, bass, accompaniment, or background from a mixed track. It aims to give users the ability to take any existing song and decompose it into separate stems (vocals, accompaniment, etc.), or to train custom separation models on their own datasets (e.g. for speech enhancement, instrument isolation, or... -
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Machine Learning Financial Laboratory
MlFinLab helps portfolio managers and traders
MlFinLab is a comprehensive Python library designed to support the development of machine learning strategies in quantitative finance and algorithmic trading. The project provides a large collection of tools that implement techniques from academic research on financial machine learning. It covers the full lifecycle of developing data-driven trading strategies, including data preprocessing, feature engineering, labeling techniques, model training, and performance evaluation. Many of the... -
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AI Platform Training and Prediction
Cloud ML Engine repo
AI Platform Training and Prediction is a collection of machine learning example projects that demonstrate how to train, deploy, and serve models using Google Cloud AI Platform and related services. It includes a wide variety of implementations across frameworks such as TensorFlow, PyTorch, scikit-learn, and XGBoost, allowing developers to explore different approaches to building ML solutions. The repository covers the full machine learning lifecycle, including data preprocessing, model... -
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Machine Learning in Asset Management
Machine Learning in Asset Management
Machine Learning in Asset Management is a research-oriented repository that explores how machine learning techniques can be applied to portfolio management and asset allocation. The project collects educational materials, code implementations, and experiments related to applying artificial intelligence methods in financial markets. It covers topics such as predictive modeling for asset prices, portfolio optimization strategies, and risk management using machine learning algorithms. The repository also includes references to academic research, tutorials, and datasets that help users understand how machine learning can enhance traditional investment strategies. ... -
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Grade School Math
8.5K high quality grade school math problems
The grade-school-math repository (sometimes called GSM8K) is a curated dataset of 8,500 high-quality grade school math word problems intended for evaluating mathematical reasoning capabilities of language models. It is structured into 7,500 training problems and 1,000 test problems. These aren’t trivial exercises — many require multi-step reasoning, combining arithmetic operations, and handling intermediate steps (e.g. “If she sold half as many in May… how many in total?”). The problems are...