Search Results for "apostila-python" - Page 18
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Showing 2663 open source projects for "apostila-python"
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Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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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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SpikingJelly
SpikingJelly is an open-source deep learning framework
SpikingJelly is an open-source deep learning framework for spiking neural networks that is primarily built on top of PyTorch and aimed at neuromorphic computing research. The project provides the components needed to build, train, and evaluate neural models that communicate through discrete spikes rather than the continuous activations used in conventional artificial neural networks. This makes it especially relevant for researchers interested in biologically inspired computing, event-driven... -
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MoBA
MoBA: Mixture of Block Attention for Long-Context LLMs
MoBA, short for Mixture of Block Attention, is an open-source research implementation of a novel attention mechanism designed to improve the efficiency of large language models processing extremely long contexts. The architecture adapts ideas from Mixture-of-Experts networks and applies them directly to the attention mechanism of transformer models. Instead of forcing each token to attend to every other token in the sequence, MoBA divides the context into blocks and dynamically routes... -
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oterm
the terminal client for Ollama
Oterm is an open-source terminal client designed to provide a lightweight command-line interface for interacting with large language models through the Ollama ecosystem. The tool allows users to chat with local AI models directly from the terminal without needing a graphical interface or web application. Its interface is designed to be simple and intuitive, enabling developers to launch conversations quickly using a single command. Oterm supports persistent chat sessions that store... -
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DeepWiki Open
AI-Powered Wiki Generator for GitHub/Gitlab/Bitbucket Repositories
DeepWiki Open is an open-source, AI-powered wiki generator that automatically creates fully navigable, richly structured wiki documentation for GitHub, GitLab, or Bitbucket repositories by combining code analysis, vector embeddings, retrieval-augmented generation (RAG), and visualization tools. Users can enter a repository URL and the system will clone the project, build semantic embeddings of its codebase, extract architecture and relationships, generate human-readable documentation, and... -
Secure File Transfer for Windows with Cerberus by RedwoodCerberus supports unlimited users and connections on a single IP, with built-in encryption, 2FA, and a browser-based web client — all deployable in under 15 minutes with a 25-day free trial.
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Dream Textures
Stable Diffusion built-in to Blender
Create textures, concept art, background assets, and more with a simple text prompt. Use the 'Seamless' option to create textures that tile perfectly with no visible seam. Texture entire scenes with 'Project Dream Texture' and depth to image. Re-style animations with the Cycles render pass. Run the models on your machine to iterate without slowdowns from a service. Create textures, concept art, and more with text prompts. Learn how to use the various configuration options to get exactly what... -
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Netron
Visualizer for neural network, deep learning, machine learning models
...Netron has experimental support for TensorFlow, PyTorch, TorchScript, OpenVINO, Torch, Arm NN, BigDL, Chainer, CNTK, Deeplearning4j, MediaPipe, ML.NET, scikit-learn, TensorFlow.js. There is an extense variety of sample model files to download or open using the browser version. It is supported by macOS, Windows, Linux, Python Server and browser. -
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TypeChat
Library for building type-safe natural language interfaces with LLMs
TypeChat is an open source library developed by Microsoft that simplifies the creation of natural language interfaces by using type definitions to structure interactions with large language models. Traditional natural language interfaces often relied on complex decision trees to interpret user intent and gather required inputs. With the rise of large language models, developers can interpret user requests more easily, but they still face challenges related to output reliability, safety, and... -
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Awesome LLM Apps
Collection of awesome LLM apps with AI Agents and RAG using OpenAI
Awesome LLM Apps is a community-curated directory of interesting, practical, and innovative applications built on or around large language models, serving as a discovery hub for developers, researchers, and enthusiasts. The list spans a wide range of categories including productivity tools, creative assistants, utilities, education platforms, research frameworks, and niche vertical apps, showcasing how generative models are being used across domains. Each entry includes a brief description,... -
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Code-Mode
Plug-and-play library to enable agents to call MCP and UTCP tools
...The repository contains both TypeScript and Python libraries, plus a code-mode-mcp component for integrating with MCP and UTCP ecosystems. Benchmarks in the README highlight improvements in latency and token cost for scenarios involving multiple tools, showing that code execution often outperforms traditional JSON-based function calling. -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameter tuning to find the best model. ... -
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CrewAI
Framework for orchestrating role-playing, autonomous AI agents
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. The power of AI collaboration has too much to offer. CrewAI is designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI... -
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Ray
A unified framework for scalable computing
...Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO. Easily build out scalable, distributed systems in Python with simple and composable primitives in Ray Core. -
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Interpretable machine learning
Book about interpretable machine learning
This book is about interpretable machine learning. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the... -
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fastai
Deep learning library
...This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai is organized around two main design goals: to be approachable and rapidly productive, while also being deeply hackable and configurable. It is built on top of a hierarchy of lower-level APIs which provide composable building blocks. -
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Ultralytics
Ultralytics YOLO
Ultralytics is a comprehensive computer vision framework that provides state-of-the-art implementations of the YOLO (You Only Look Once) family of models, enabling developers to perform tasks such as object detection, segmentation, classification, tracking, and pose estimation within a unified system. It is designed to be fast, accurate, and easy to use, offering both command-line and Python-based interfaces for training, validation, and deployment of machine learning models. The framework supports a full end-to-end workflow, including dataset preparation, model training, evaluation, and export to various deployment formats. Its architecture emphasizes performance optimization, balancing speed and accuracy to support real-time applications across industries. ... -
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Toad
Unified terminal AI tool for exploring and editing codebases
...It allows developers to interact with AI models directly inside the command line, making it easier to explore, understand, and modify codebases without leaving the terminal. Built in Python, it focuses on transparency and control by letting users load context intentionally and inspect how the AI processes files. Toad supports structured conversations, enabling navigation through code with clear references instead of opaque outputs. Inspired by notebook-style workflows, it allows reuse of previous interactions and exporting of results. ... -
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Machine learning basics
Plain python implementations of basic machine learning algorithms
...The repository includes notebooks that demonstrate classic algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, support vector machines, and clustering techniques. Each notebook typically combines explanatory text, Python code, and visualizations to illustrate how the algorithm operates and how it can be applied to datasets. -
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Bespoke Curator
Synthetic data curation for post-training and data extraction
Curator is an open-source Python library designed to build synthetic data pipelines for training and evaluating machine learning models, particularly large language models. The system helps developers generate, transform, and curate high-quality datasets by combining automated generation with structured validation and filtering. It supports workflows where models are used to produce synthetic examples that can later be refined into reliable training datasets for reasoning, question answering, or structured information extraction tasks. ... -
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OpenTinker
OpenTinker is an RL-as-a-Service infrastructure for foundation models
...It introduces a centralized scheduler to manage distributed training jobs and shared compute resources, enabling workloads like reinforcement learning, supervised fine-tuning, and inference to run across multiple settings. The architecture supports a range of single-turn and multi-turn agentic tasks with a design that abstracts away infrastructure complexity while offering flexible Python APIs to define environments and workflows. -
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Z80-μLM
Z80-μLM is a 2-bit quantized language model
Z80-μLM is a retro-computing AI project that demonstrates a tiny language model (Z80-μLM) engineered to run on an 8-bit Z80 CPU by aggressively quantizing weights down to 2-bit precision. The repository provides a complete workflow where you train or fine-tune conversational models in Python, then export them into a format that can be executed on classic Z80 systems. A key deliverable is producing CP/M-compatible .COM binaries, enabling a genuinely vintage “chat with your computer” experience on real hardware or accurate emulators. The project sits at the intersection of machine learning and systems constraints, showing how model architecture, quantization, and inference code generation can be adapted to extreme memory and compute limits. ... -
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Anthropic's Original Performance
Anthropic's original performance take-home, now open for you to try
...This take-home includes starter code, tests, and tools to debug performance, aiming to measure how effectively one can apply algorithmic improvements and optimizations. Because it’s framed around beating baseline scores — and even outperforming previous automated systems — it encourages both deep knowledge of Python and creative problem-solving. -
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Dendrite
Tools to build web AI agents that can authenticate
Dendrite Python SDK is a toolkit for building web AI agents that can authenticate, interact with, and extract data from any website, facilitating web automation tasks. -
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NeMo Curator
Scalable data pre processing and curation toolkit for LLMs
...At the core of the NeMo Curator is the DocumentDataset which serves as the the main dataset class. It acts as a straightforward wrapper around a Dask DataFrame. The Python library offers easy-to-use methods for expanding the functionality of your curation pipeline while eliminating scalability concerns. -
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kotaemon
An open-source RAG-based tool for chatting with your documents
An open-source clean & customizable RAG UI for chatting with your documents. Built with both end users and developers in mind. This project serves as a functional RAG UI for both end users who want to do QA on their documents and developers who want to build their own RAG pipeline. -
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AgentOps
Python SDK for agent monitoring, LLM cost tracking, benchmarking, etc.
Industry-leading developer platform to test and debug AI agents. We built the tools so you don't have to. Visually track events such as LLM calls, tools, and multi-agent interactions. Rewind and replay agent runs with point-in-time precision. Keep a full data trail of logs, errors, and prompt injection attacks from prototype to production. Native integrations with the top agent frameworks.
