Search Results for "deep" - Page 8
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The Grand Complete Data Science Guide
Data Science Guide With Videos And Materials
The Grand Complete Data Science Materials is a repository curated by a data-science educator that aggregates a wide range of learning resources — from basic programming and math foundation to advanced topics in machine learning, deep learning, natural language processing, computer vision, and deployment practices — into a structured, centralized collection aimed at learners seeking a comprehensive path to data science mastery. The repository bundles tutorials, lecture notes, project outlines, course materials, and references across topics like Python, statistics, ML algorithms, deep learning, NLP, data preprocessing, model evaluation, and real-world problem solving. ... -
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Tongyi DeepResearch
Tongyi Deep Research, the Leading Open-source Deep Research Agent
DeepResearch (Tongyi DeepResearch) is an open-source “deep research agent” developed by Alibaba’s Tongyi Lab designed for long-horizon, information-seeking tasks. It’s built to act like a research agent: synthesizing, reasoning, retrieving information via the web and documents, and backing its outputs with evidence. The model is about 30.5 billion parameters in size, though at any given token only ~3.3B parameters are active. -
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DeepSpeed MII
MII makes low-latency and high-throughput inference possible
MII makes low-latency and high-throughput inference possible, powered by DeepSpeed. The Deep Learning (DL) open-source community has seen tremendous growth in the last few months. Incredibly powerful text generation models such as the Bloom 176B, or image generation model such as Stable Diffusion are now available to anyone with access to a handful or even a single GPU through platforms such as Hugging Face. While open-sourcing has democratized access to AI capabilities, their application is still restricted by two critical factors: inference latency and cost. ... -
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DocArray
The data structure for multimodal data
DocArray is a library for nested, unstructured, multimodal data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer multimodal data with a Pythonic API. Door to multimodal world: super-expressive data structure for representing complicated/mixed/nested text, image, video, audio, 3D mesh data. The foundation data structure of Jina, CLIP-as-service, DALL·E Flow, DiscoArt etc. ... -
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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Raster Vision
Open source framework for deep learning satellite and aerial imagery
Raster Vision is an open source framework for Python developers building computer vision models on satellite, aerial, and other large imagery sets (including oblique drone imagery). There is built-in support for chip classification, object detection, and semantic segmentation using PyTorch. Raster Vision allows engineers to quickly and repeatably configure pipelines that go through core components of a machine learning workflow: analyzing training data, creating training chips, training... -
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Jina
Build cross-modal and multimodal applications on the cloud
Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP,... -
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iCSS
More than CSS
iCSS is a large curated repository of advanced CSS techniques, creative experiments, and deep dives into modern web animation and styling capabilities. Rather than being a traditional library, the project functions as an educational and inspirational knowledge base that explores unusual, powerful, or overlooked CSS features. The content covers topics such as layout tricks, animation patterns, visual effects, accessibility considerations, and emerging CSS standards. -
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Hubble
Network, Service & Security Observability for Kubernetes using eBPF
...By building on top of Cilium, Hubble can leverage eBPF for visibility. By leveraging eBPF, all visibility is programmable and allows for a dynamic approach that minimizes overhead while providing deep and detailed insight where required. Hubble has been created and specifically designed to make best use of these new eBPF powers. -
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ProxyAI
The leading open-source AI copilot for JetBrains
...It allows developers to connect to a wide range of language models, including cloud-based services and locally hosted models, enabling both online and fully offline workflows depending on user preferences. The platform emphasizes deep integration with the developer’s environment, providing context-aware assistance by referencing files, folders, Git history, and even external documentation during interactions. ProxyAI enhances productivity by enabling natural language-driven code editing, intelligent autocompletion, and automated generation of commit messages, all within the IDE interface. ... -
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Ren'Py
The Ren'Py Visual Novel Engine
...Its scripting language is designed to be readable and intuitive, allowing writers and creators to define scenes, dialogues, branching choices, character expressions, and events without deep programming knowledge, while still offering the ability to embed Python for advanced control and custom gameplay features. The engine handles essential visual novel conventions like save and load systems, rollback to previous text, scene transitions, and UI menus, so creators can focus on the story and player experience. Because it’s built on Python and widely supported across platforms, Ren’Py games can run on Windows, macOS, Linux, mobile devices, and even in browsers with HTML5 builds, helping developers reach a broad audience. -
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JEPA
PyTorch code and models for V-JEPA self-supervised learning from video
JEPA (Joint-Embedding Predictive Architecture) captures the idea of predicting missing high-level representations rather than reconstructing pixels, aiming for robust, scalable self-supervised learning. A context encoder ingests visible regions and predicts target embeddings for masked regions produced by a separate target encoder, avoiding low-level reconstruction losses that can overfit to texture. This makes learning focus on semantics and structure, yielding features that transfer well... -
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Perfect Roadmap To Learn Data Science
Basic To Intermediate Python data science guide
Perfect Roadmap To Learn Data Science In 2025 is an extended, updated learning pathway curated for the modern data-science landscape — blending classical data-analysis, statistics, machine learning, deep learning, computer vision, NLP, as well as current deployment and MLOps practices to prepare learners for data-science careers in 2025. The roadmap is organized to guide learners systematically: starting with Python fundamentals and math/statistics, then progressing through classical machine-learning, deep-learning, data preprocessing, feature engineering, and onto domain-specific applications like computer vision or NLP, ending with deployment, real-world project construction, and best practices for production readiness. ... -
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Synapse Machine Learning
Simple and distributed Machine Learning
SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit, and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of data sources. ... -
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Exclusively Dark Image Dataset
ExDARK dataset is the largest collection of low-light images
...The dataset was created to address the lack of large-scale low-light datasets available for research in object detection, recognition, and enhancement. It has been widely used in studies of low-light image enhancement, deep learning approaches, and domain adaptation for vision models. Researchers can also explore its associated source code for low-light image enhancement tasks, making it an essential resource for advancing work in night-time and low-light visual recognition. -
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spyder
The scientific Python development environment
...It features a unique combination of the advanced editing, analysis, debugging, and profiling functionality of a comprehensive development tool with the data exploration, interactive execution, deep inspection, and beautiful visualization capabilities of a scientific package. Spyder’s multi-language Editor integrates a number of powerful tools right out of the box for an easy to use, efficient editing experience. The Editor’s key features include syntax highlighting (pygments); real-time code and style analysis (pyflakes and pycodestyle); on-demand completion, calltips and go-to-definition features (rope and jedi); a function/class browser, horizontal and vertical splitting, and much more. -
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OpenCV
Open Source Computer Vision Library
OpenCV (Open Source Computer Vision Library) is a comprehensive open-source library for computer vision, machine learning, and image processing. It enables developers to build real-time vision applications ranging from facial recognition to object tracking. OpenCV supports a wide range of programming languages including C++, Python, and Java, and is optimized for both CPU and GPU operations. -
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Llama Stack
Composable building blocks to build Llama Apps
Llama-Stack is an open-source framework designed to facilitate the deployment and fine-tuning of large language models (LLMs) for various natural language processing tasks. -
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TechCPP
C++ learning and interview guide aimed at back-end systems developers
TechCPP is a comprehensive C++ learning and interview guide aimed at back-end and systems developers preparing for professional roles. It gathers frequently asked concepts and deep dives—value categories (lvalue/rvalue), perfect forwarding, casts, memory models, atomics, and more—into a structured, readable format. The material goes beyond syntax to discuss performance, optimization techniques, and how standard library containers are implemented under the hood. You’ll also find practical debugging and tooling advice, such as using gdb to diagnose deadlocks or reasoning about concurrency primitives. ... -
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The Hypersim Dataset
Photorealistic Synthetic Dataset for Holistic Indoor Scene
Hypersim is a large-scale, photorealistic synthetic dataset and tooling suite for indoor scene understanding research. It provides richly annotated renderings—RGB, depth, surface normals, instance and semantic segmentations, and material/lighting metadata—produced from high-fidelity virtual environments. The dataset spans diverse furniture layouts, room types, and camera trajectories, enabling robust training for geometry, segmentation, and SLAM-adjacent tasks. Rendering pipelines and... -
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Image Fusion
Deep Learning-based Image Fusion: A Survey
This repository is a survey / code collection centered on deep learning–based image fusion (e.g. fusing infrared + visible light images, multi-modal fusion) methods. It catalogs many fusion algorithms (e.g. DenseFuse, FusionGAN, NestFuse, etc.), links to code implementations, and describes evaluation metrics. The repository includes a “General Evaluation Metric” subfolder containing objective fusion metrics. -
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Dexter
An autonomous agent for deep financial research
Dexter is an autonomous agent tailored for deep financial research: you pose complex financial questions (for example, about a company’s revenue growth or financial ratios) and Dexter breaks them down into structured research tasks, fetches relevant real-time data (e.g. income statements, cash flows), performs analysis, and returns data-backed answers. It uses a multi-agent architecture with components such as a planning agent (to decompose queries), an action agent (to run tasks & fetch data), and self-validation mechanisms: after getting results, Dexter checks its own outputs and refines them until it is confident about its answer. ... -
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LLMChat
Unified interface for AI chat, Agentic workflows and more
...One of its primary goals is to support sophisticated research workflows that combine conversational AI with information retrieval and reasoning tools. The platform includes specialized interaction modes such as deep research analysis and enhanced search capabilities that help users explore complex topics more effectively. It also incorporates agent-style workflows that allow the system to orchestrate multiple steps of reasoning or data retrieval during a conversation. -
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bitsandbytes
Accessible large language models via k-bit quantization for PyTorch
...Built primarily for the PyTorch ecosystem, the library introduces advanced quantization techniques that allow models to operate using reduced numerical precision while maintaining high accuracy. These optimizations enable large language models and other deep learning architectures to run on hardware with limited memory resources, including consumer-grade GPUs. The project includes specialized optimizers and quantized matrix operations that significantly reduce the memory footprint of training and inference workloads. By lowering the hardware requirements needed to work with large models, bitsandbytes helps make modern AI development more accessible to researchers and engineers. ... -
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NVIDIA Earth2Studio
Open-source deep-learning framework
...The toolkit makes it easy to run deterministic and ensemble forecasts, swap models interchangeably, and process large geophysical datasets with Xarray structures, enabling experimentation with state-of-the-art deep learning models for climate and atmospheric prediction. Users can extend Earth2Studio with optional model packs, advanced data interfaces, statistical operators, and backend integrations that support flexible workflows from simple tests to large-scale operational inference. -
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Rust Course
It has been the world's most popular language for 8 consecutive years
...The course is carefully designed with a structured catalog, vivid and approachable language, and an engaging style that avoids the dry and mechanical tone of many technical books. It covers the basics of Rust, such as ownership, borrowing, lifetimes, traits, and generics, but also dives deep into advanced topics like performance optimization, linked list implementations, async programming with Tokio, standard library internals, Cargo usage, and WebAssembly development. The project emphasizes practical learning through exercises, helping users approach Rust study as if it were a university course. It also provides a "Cookbook" section of practical code snippets for common tasks such as file operations, regex handling, and database interactions, allowing learners to quickly reference solutions without searching externally.
