Search Results for "ekho-data" - Page 26
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Showing 3593 open source projects for "ekho-data"
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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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linkedin2username
Generate probable usernames from LinkedIn company employee lists
...Unlike tools that rely on official APIs, linkedin2username operates as a pure web scraper and therefore does not require API keys. The script uses Selenium to automate browser interactions and perform searches within LinkedIn to gather employee data. -
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llms-from-scratch-cn
Build a large language model from 0 only with Python foundation
...Rather than focusing on using pre-trained models through APIs, the project emphasizes understanding the internal mechanisms of modern language models, including tokenization, attention mechanisms, transformer architecture, and training workflows. Through a collection of notebooks, code examples, and translated learning materials, users can explore how to implement components such as multi-head attention, data loaders, and training pipelines using Python and PyTorch. -
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Book5_Essentials-Probability-Statistics
The book 5 of statistics in simplicity
...Like the other books in the series, it blends mathematical explanation with Python-based experimentation. Overall, the project provides a practical statistical foundation for students advancing into AI and data science. -
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UCP Python SDK
The official Python SDK for UCP
...UCP itself is a modern, open-source standard that empowers seamless commerce interactions between platforms, AI agents, merchants, and payment providers without requiring bespoke integrations for every participant in the commerce ecosystem. This SDK provides Pydantic models for UCP schemas, making it easy for Python developers to construct, validate, and serialize protocol messages and data structures according to the UCP specification. By adhering to the official protocol standards, applications built on this SDK can participate in tasks like capability discovery, checkout flows, order management, and more, while remaining interoperable across different UCP implementations and surfaces. -
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Android Emulator Container Scripts
Minimal scripts to run the emulator in a container for various systems
...A built-in WebRTC bridge lets you stream the emulator screen to a browser with interactive input, which is ideal for CI dashboards, remote debugging, or demo environments. The project focuses on reproducibility and scale: you define which system image to boot, how to persist or reset data, and how many instances to run, then schedule them like any other workload. GPU acceleration, audio, and sensors can be enabled depending on your host and cluster capabilities, while fallbacks like SwiftShader keep things usable when no GPU is available. -
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X's Recommendation Algorithm
Source code for the X Recommendation Algorithm
...Written primarily in Scala, it shows the architecture of large-scale recommendation systems, including candidate sourcing, ranking, and heuristics. While certain components (such as safety layers, spam detection, or private data) are excluded, the release provides valuable insights into the design of real-world machine learning–driven ranking systems. The project is intended as a reference for researchers, developers, and the public to study, experiment with, and better understand the mechanisms behind social media content. -
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ContextGem
ContextGem: Effortless LLM extraction from documents
ContextGem is an open-source framework designed to simplify the extraction of structured data and insights from documents using large language models (LLMs). It provides a flexible, intuitive API that minimizes boilerplate code, enabling developers to build complex extraction workflows efficiently. ContextGem supports various document formats and integrates with multiple LLM providers, making it a versatile tool for tasks like contract analysis, anomaly detection, and information retrieval. -
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Shapash
Explainability and Interpretability to Develop Reliable ML models
Shapash is a Python library dedicated to the interpretability of Data Science models. It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can more easily understand their models, share their results and easily document their projects in an HTML report. End users can understand the suggestion proposed by a model using a summary of the most influential criteria. -
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TextAttack
Python framework for adversarial attacks, and data augmentation
Generating adversarial examples for NLP models. TextAttack is a Python framework for adversarial attacks, data augmentation, and model training in NLP. -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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Marvin
A batteries-included library for building AI-powered software
...With AI functions, you don't have to write complex code for tasks like extracting entities from web pages, scoring sentiment, or categorizing items in your database. Just describe your needs, call the function, and you're done. AI functions work with native data types, so you can seamlessly integrate them into any codebase and chain them into sophisticated pipelines. In addition to AI functions, Marvin also introduces more flexible bots. Bots are highly capable AI assistants that can be given specific instructions and personalities or roles. -
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OpenFold
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction
...OpenFold is trainable in full precision, half precision, or bfloat16 with or without DeepSpeed, and we've trained it from scratch, matching the performance of the original. We've publicly released model weights and our training data — some 400,000 MSAs and PDB70 template hit files — under a permissive license. Model weights are available via scripts in this repository while the MSAs are hosted by the Registry of Open Data on AWS (RODA). -
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DeepVariant
DeepVariant is an analysis pipeline that uses a deep neural networks
DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file. DeepTrio is a deep learning-based trio variant caller built on top of DeepVariant. ... -
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DeepCTR
Package of deep-learning based CTR models
...You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based techniques have been widely used in CTR prediction task. The data in CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Since DNN are good at handling dense numerical features,we usually map the sparse categorical features to dense numerical through embedding technique. -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of... -
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tqdm
A Fast, Extensible Progress Bar for Python and CLI
tqdm is a fast, extensible progress bar for Python and CLI that enables you to see the progress of your loops in a clear and smart way. Simply wrap any iterable with tqdm(iterable), and sit back and watch that progress meter go! tqdm can be wrapped around any iterable, or executed as a module with pipes. Just by inserting tqdm (or python -m tqdm) between pipes will pass through all stdin to stdout while printing progress to stderr. tqdm does not require any dependencies, has a very... -
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Pixal3D
Pixel-Aligned 3D Generation from Images
Pixal3D is a TencentARC research project for generating high-fidelity 3D assets from a single input image. It addresses a key weakness in image-to-3D generation: many models produce plausible 3D shapes but fail to preserve pixel-level faithfulness to the original image. Pixal3D improves this by explicitly lifting image features into 3D through back-projection, creating clearer correspondences between the input pixels and the generated asset. The system is designed to produce detailed... -
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List of Free Learning Resources
Freely available programming books
...Maintained by the community, it organizes materials by topic, language, and skill level, making it easy to discover learning resources. The repository includes content on software development, computer science, data science, and more. It is continuously updated with new resources contributed by developers worldwide. The project emphasizes accessibility and open education, providing high-quality materials without cost. It serves as a central hub for self-learners and professionals alike. Its structured organization makes it a widely used reference for learning programming. -
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TrustGraph
Deploy reasoning AI agents powered by agentic graph RAG in minutes
TrustGraph is an AI-driven framework designed to assess and visualize trust relationships within networks, aiding in the analysis of trustworthiness and influence among entities. -
19
nuwa-skill
Mental models, decision heuristics, expressing DNA
...It provides a framework for organizing capabilities into reusable units that can be invoked dynamically depending on context or user input. The project is designed to integrate with AI systems, enabling them to perform structured tasks such as data retrieval, processing, or interaction with external services. It emphasizes modularity and extensibility, allowing developers to create new skills and plug them into the system without disrupting existing functionality. Nuwa Skill also supports orchestration, enabling multiple skills to work together to accomplish more complex objectives. ... -
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LLM Vision
Visual intelligence for your home.
...Instead of relying only on traditional object detection pipelines, it allows users to send prompts about visual content and receive contextual descriptions or answers about what is happening in camera footage. The system can process events from surveillance platforms such as Frigate and convert them into meaningful summaries, notifications, or structured data for automation workflows. It also maintains a timeline of analyzed camera events that can be displayed in dashboards or queried through the assistant interface. -
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PowerTrader_AI
Fully automated crypto trading powered by a custom price prediction AI
PowerTrader_AI is a fully open-source, automated cryptocurrency trading bot that combines a custom price prediction AI with a structured and tiered dollar-cost averaging (DCA) strategy to make real trading decisions on behalf of users. It continuously analyzes market data to forecast high and low price levels across multiple timeframes, using those predictions to determine when to open, scale into, or close positions automatically, which aims to take emotion out of trading and enforce discipline. The bot integrates with popular exchange APIs so it can execute real buy and sell orders, manage risk, and maintain position sizing without manual intervention, making it suitable for algorithmic traders who want hands-off operations. ... -
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/last30days
Claude Code skill that researches any topic across Reddit + X
/last30days is a specialized Claude Code skill designed to research current trends and practices across Reddit, X, and the wider web from the last 30 days, synthesize that data, and produce copy-paste-ready prompts or summaries that reflect what the community is actually talking about now. Rather than returning generic model responses, it intelligently analyzes social media and community discussions to identify what’s genuinely trending or working in practice across topics ranging from prompt techniques to tool usage or cultural trends. ... -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
...Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before they pass into a neural network (if you use augmentation). The general recommendation is to use suitable augs for your data and as many as possible, then after some time of training disable the most destructive (for image) augs. You can turn on automatic mixed precision with one flag --amp. ... -
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SpeechRecognition
Speech recognition module for Python
Library for performing speech recognition, with support for several engines and APIs, online and offline. Recognize speech input from the microphone, transcribe an audio file, save audio data to an audio file. Show extended recognition results, calibrate the recognizer energy threshold for ambient noise levels (see recognizer_instance.energy_threshold for details). Listening to a microphone in the background, various other useful recognizer features. The easiest way to install this is using pip install SpeechRecognition. ... -
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django-pgtrigger
Write Postgres triggers for your Django models
...It allows developers to create database-level logic like automatic field updates, auditing, or validation without writing raw SQL. It’s ideal for teams that want stronger data integrity while keeping logic version-controlled.