Search Results for "deep" - Page 28
Sort By:
Showing 1152 open source projects for "deep"
View related business solutions-
AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
Mapus
A map tool with real-time collaboration
...The design philosophy favors lightweight integration and ease-of-use, enabling quick prototyping of map-heavy features such as user location display, interactive storytelling, data visualization on maps, or event mapping, without requiring deep GIS expertise. -
2
3D-Machine-Learning
A resource repository for 3D machine learning
...The project acts as a curated research directory that includes papers, datasets, tutorials, and software tools relevant to the emerging field of 3D machine learning. This interdisciplinary domain combines ideas from computer vision, computer graphics, and deep learning to analyze and generate three-dimensional structures. The repository includes references to important research papers covering topics such as point cloud processing, 3D reconstruction, shape analysis, and scene understanding. It also organizes links to university courses and other educational materials that explore machine learning methods for 3D data. ... -
3
SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...This library provides default pre-processing, predict and postprocessing for certain MXNet model types and utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). ... -
4
Flexjson is a lightweight library for serializing Java objects into JSON. What's different about Flexjson is it's control over what gets serialized allowing both deep and shallow copies of objects.
-
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.
-
5
ASRT Speech Recognition
A Deep-Learning-Based Chinese Speech Recognition System
ASRT is an end-to-end deep-learning Chinese ASR system built with TensorFlow/Keras, using convolution + CTC and a Max-Entropy HMM language model. It provides a REST/gRPC server backend and client SDKs in multiple languages (Python, Java, Go, Windows). Notably lightweight, it performs well without needing GPU acceleration and runs across platforms, targeting developers and researchers building Chinese voice interfaces. -
6
Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new. ... -
7
MXNet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning
Apache MXNet is a scalable, efficient open-source deep learning framework—offering a flexible hybrid programming model (symbolic + imperative) and supporting a wide array of languages—designed for training and deploying neural networks across heterogeneous systems. Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. -
8
DeepLearning Tutorial
Deep Learning Tutorial, Excellent Articles, Deep Learning Tutorial
...The repository organizes these materials into structured tutorials and references that allow readers to explore deep learning concepts progressively. Many of the resources include explanations of common model architectures used in computer vision and artificial intelligence. The project also collects recommended articles and educational documents that expand on deep learning theory and practical implementation strategies. -
9
Deep Learning course
Slides and Jupyter notebooks for the Deep Learning lectures
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
10
paip-lisp
Lisp code for the textbook "Paradigms of Artificial Intelligence"
...It is valuable for readers who want to study the original code while working through the text or revisiting older AI ideas. The code also serves as a historical reference for how AI programming was taught before today’s deep-learning-centered ecosystem. paip-lisp is best suited for learners interested in Lisp, symbolic AI, and the foundations of practical AI programming. -
11
sRNAWorkbench
The UEA sRNA Workbench
A suite of tools for analysing small RNA (sRNA) data from Next Generation Sequencing devices. Including expression profiling of known mirco RNA (miRNA), identification of novel miRNA in deep-sequencing data and identification of other interesting landmarks within high-throughput genetic data -
12
Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
13
Interactive Deep Colorization
Deep learning software for colorizing black and white images
Interactive Deep Colorization is a software project for colorizing black-and-white (grayscale) images using deep learning, allowing users to add a few hints (e.g. scribbles) and get a plausible, fully colorized output. The idea is to merge automatic colorization (via neural networks) with optional user guidance — so if the automatic model’s guess isn’t quite right, the user can nudge colors via hints to steer the result, achieving more controlled, satisfying outputs. -
14
DeeProtGO
DeeProtGO is a deep learning model for predicting GO terms of proteins
This project contains the source code of DeeProtGO as well as an example of its use when predicting GO terms of the biological process sub-ontology for eukaryotic proteins. -
15
The fastai book
The fastai book, published as Jupyter Notebooks
These notebooks cover an introduction to deep learning, fastai, and PyTorch. fastai is a layered API for deep learning; for more information, see the fastai paper. These notebooks are used for a MOOC and form the basis of this book, which is currently available for purchase. It does not have the same GPL restrictions that are on this repository. The code in the notebooks and python .py files is covered by the GPL v3 license; see the LICENSE file for details. -
16
LayoutParser
A Unified Toolkit for Deep Learning Based Document Image Analysis
With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process. A complete instruction for installing the main Layout Parser library and auxiliary components. Learn how to load DL Layout models and use them for layout detection. -
17
pyTorch Tutorials
Build your neural network easy and fast
pyTorch Tutorials is an open-source collection of hands-on tutorials designed to teach developers how to build neural networks with the PyTorch framework. It covers the fundamentals of PyTorch from basic tensor operations to constructing full neural network models, making it suitable for beginners and intermediate learners alike. The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks,... -
18
TensorFlowOnSpark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters
By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. -
19
edge-TTS-record
Tool that can record speech synthesis
...Users can type or paste text, preview the speech, and then trigger the recorder; the system automatically captures the audio output from the browser and writes it to a WAV file. The tool includes a small GUI (built with Aardio) and aims to be plug-and-play — after downloading the .exe you can immediately start using it without deep configuration. It is cloud-based in the sense that it relies on Edge’s online TTS service, so internet connection is required; but once recorded, the audio is local. -
20
Flashlight library
A C++ standalone library for machine learning
Flashlight is a fast, flexible machine learning library written entirely in C++ by Facebook AI Research and the creators of Torch, TensorFlow, Eigen, and Deep Speech. Native support in C++ and simple extensibility make Flashlight a powerful research framework that's hackable to its core and enables fast iteration on new experimental setups and algorithms with little unopinionated and without sacrificing performance. In a single repository, Flashlight provides apps for research across multiple domains. ... -
21
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. ... -
22
irs-elcho
Telemetryczny system monitoringu stanu technicznego urządzeń
Celem projektu jest opracowanie telemetrycznego systemu monitoringu stanu technicznego urządzeń typu chłodnie wentylatorowe, który wykorzystując algorytmy typu deep learning będzie stanowił element strategii predykcyjnego utrzymania ruchu. System wpisuje się w ideę Przemysłu 4.0 poprzez umożliwianie monitoringu i predykcji stanu urządzeń. Projekt zakłada badania przemysłowe i prace rozwojowe nad systemem dokonującym kompleksowej akwizycji i analizy danych dot. stanu działania urządzenia. Rejestracji będą podlegać wielkości fizyczne obrazujące działanie urządzeń przy pomocy zbioru czujników. ... -
23
Deeplearning.ai
Study notes, summaries, and auxiliary materials for deep learning
...Many learners use it to supplement course videos, reinforcing concepts before implementing assignments or projects. As a consolidated guide, it reduces context-switching and helps build a durable mental model of deep learning fundamentals. -
24
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. -
25
YOLOv3
Object detection architectures and models pretrained on the COCO data
...Export and deploy your YOLOv5 model with just 1 line of code. There are also loads of quickstart guides and tutorials available to get your model where it needs to be. Create state of the art deep learning models with YOLOv5
