Search Results for "deep" - Page 27
Sort By:
Showing 1152 open source projects for "deep"
View related business solutions-
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.
-
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.
-
1
OpenPy-DELF
Aplicación para realizar pronósticos de carga eléctrica a corto
Es una aplicación para realizar pronósticos de carga eléctrica a corto plazo, basado en una nueva combinación efectiva de técnicas de aprendizaje profundo “Deep Learning”, que considera los desafíos de las redes eléctricas inteligentes. Esta primer versión beta permite realizar pronósticos de carga eléctrica, desde un enfoque determinístico o probabilístico, para un horizonte de tiempo arbitrario a corto plazo. -
2
Elephas
Distributed Deep learning with Keras & Spark
Elephas is an extension of Keras, which allows you to run distributed deep learning models at scale with Spark. Elephas currently supports a number of applications. Elephas brings deep learning with Keras to Spark. Elephas intends to keep the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models, which can be run on massive data sets. Elephas implements a class of data-parallel algorithms on top of Keras, using Spark's RDDs and data frames. ... -
3
Coqui STT
The deep learning toolkit for speech-to-text
Coqui STT is a fast, open-source, multi-platform, deep-learning toolkit for training and deploying speech-to-text models. Coqui STT is battle-tested in both production and research. Multiple possible transcripts, each with an associated confidence score. Experience the immediacy of script-to-performance. With Coqui text-to-speech, production times go from months to minutes. With Coqui, the post is a pleasure. -
4
dirsearch
Web path scanner
An advanced command-line tool designed to brute force directories and files in webservers, AKA web path scanner. Wordlist is a text file, each line is a path. About extensions, unlike other tools, dirsearch only replaces the %EXT% keyword with extensions from -e flag. For wordlists without %EXT% (like SecLists), -f | --force-extensions switch is required to append extensions to every word in wordlist, as well as the /. To use multiple wordlists, you can separate your wordlists with commas.... -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
5
Weave Scope
Monitoring, visualization and management for Docker and Kubernetes
Understand your application quickly by seeing it in a real-time interactive display. Pick open-source or cloud-hosted options. Weave Scope automatically detects processes, containers, hosts. No kernel modules, no agents, no special libraries, no coding. Seamless integration with Docker, Kubernetes, DCOS and AWS ECS. See your Docker hosts, containers and services in real-time. Easily identify and correct issues to ensure the stability and performance of your containerized applications. View... -
6
Bullet Physics SDK
Real-time collision detection and multi-physics simulation for VR
...The simulator allows for hybrid simulation with neural networks. It allows different automatic differentiation backends, for forward and reverse mode gradients. TDS can be trained using Deep Reinforcement Learning, or using Gradient based optimization (for example LFBGS). In addition, the simulator can be entirely run on CUDA for fast rollouts, in combination with Augmented Random Search. This allows for 1 million simulation steps per second. It is highly recommended to use PyBullet Python bindings for improved support for robotics, reinforcement learning and VR. ... -
7
Emb-GAM
An interpretable and efficient predictor using pre-trained models
Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. -
8
CommandlineConfig
A library for users to write configurations in Python
...One of its core strengths is the ability to override configuration values directly from the command line, making it convenient to run many experimental variants without editing files repeatedly. The library supports arbitrarily deep nested structures, type handling, enumerated value constraints, and even tuple types, which are common in ML experiment setups. It also includes features for automatic version checking and convenient help output, so users can quickly see available parameters and their descriptions via a -h flag. -
9
Entitas Game Engine
Entitas is a super fast Entity Component System (ECS) Framework
Entitas is a super fast Entity Component System Framework (ECS) specifically made for C# and Unity. Internal caching and blazing-fast component access make it second to none. Several design decisions have been made to work optimally in a garbage-collected environment and to go easy on the garbage collector. Entitas comes with an optional code generator which radically reduces the amount of code you have to write and makes your code read like well-written prose. Entitas is fast, and light and... -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
-
10
Auto-PyTorch
Automatic architecture search and hyperparameter optimization
...Details about Auto-PyTorch for multi-horizontal time series forecasting tasks can be found in the paper "Efficient Automated Deep Learning for Time Series Forecasting" (also see below for bibtex ref). -
11
Hello AI World
Guide to deploying deep-learning inference networks
...Ready to dive into deep learning? It only takes two days. We’ll provide you with all the tools you need, including easy to follow guides, software samples such as TensorRT code, and even pre-trained network models including ImageNet and DetectNet examples. Follow these directions to integrate deep learning into your platform of choice and quickly develop a proof-of-concept design. -
12
DeepMind Educational Resources
DeepMind's repo of educational notebooks for learning AI and research
Educational is an open collection of interactive tutorials created by Google DeepMind to make the fundamentals of machine learning and artificial intelligence accessible to learners of all backgrounds. The repository provides hands-on, beginner-friendly resources that introduce essential AI concepts through Google Colab notebooks, combining intuitive explanations with executable code. The tutorials cover a broad range of topics—from foundational Python programming and data handling to... -
13
AirSim
A simulator for drones, cars and more, built on Unreal Engine
...It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim's development is oriented towards the goal of creating a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. For this purpose, AirSim also exposes APIs to retrieve data and control vehicles in a platform independent way. AirSim is fully enabled for multiple vehicles. This capability allows you to create multiple vehicles easily and use APIs to control them. -
14
Codeball AI
AI Code Review that finds bugs and fast-tracks your code
...Fully customizable and programmable with GitHub Actions. Codeball Actions are built on multiple smaller building blocks, that are heavily configurable through GitHub Actions. Codeball uses a deep learning model that has been trained on over 1 million Pull Requests. For each contribution, it considers hundreds of inputs. Codeball is optimized for precision, which means it only approves contributions that it's really confident in. -
15
Spring Boot Demo
A project for in-depth study and practical application of Spring Boot
This repository is a hands-on, “deep learning by doing” collection of Spring Boot demos that you can run and study module by module. It currently includes 66 planned integrations (with 55 completed) spanning monitoring, logging, templating, data access, caching, messaging, scheduling, search, security, and more. The master branch targets Spring Boot 2.1.0.RELEASE with a parent POM that centralizes common dependency versions; the older v-1.5.x branch is frozen and its contents are being migrated to master. ... -
16
baloo
Expressive end-to-end HTTP API testing made easy in Go
Expressive and versatile end-to-end HTTP API testing made easy in Go (golang), built on top of the gentleman HTTP client toolkit. -
17
Reinforcement Learning Methods
Simple Reinforcement learning tutorials
Reinforcement-Learning-with-TensorFlow is an educational repository that walks through key reinforcement learning algorithms implemented in TensorFlow. It provides clear code examples for foundational techniques like Q-learning, policy gradients, deep Q-networks, actor-critic methods, and value function approximation within familiar simulation environments. Each algorithm is structured with readable code, explanatory comments, and corresponding environment interaction loops so learners can easily trace how actions, rewards, and model updates connect. The project also includes demo scripts that visualize learning curves and allow students to observe policy improvement over training iterations. ... -
18
Chainer
A flexible deep learning framework
Chainer is a Python-based deep learning framework. It provides automatic differentiation APIs based on dynamic computational graphs as well as high-level APIs for neural networks. -
19
tf2_course
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course
tf2_course provides the notebooks for the “Deep Learning with TensorFlow 2 and Keras” course authored by the same author, Aurélien Géron. It is structured as a teaching toolkit: you’ll find notebooks covering neural networks with Keras, lower-level TensorFlow APIs, data loading & preprocessing, convolutional and recurrent networks, and deployment/distribution of models. The material is intended for learners who already have foundational knowledge of ML and wish to deepen their understanding of deep learning frameworks and practices. ... -
20
Python Machine Learning 3rd Ed.
The "Python Machine Learning (3rd edition)" book code repository
...These examples are designed to illustrate how machine learning algorithms operate internally and how they can be applied to real datasets. Many examples rely on widely used libraries such as NumPy, scikit-learn, and deep learning frameworks to demonstrate modern machine learning workflows. -
21
* To cite this dataset refer to http://dx.doi.org/10.12785/ijcds/130177 Mazari, A. C., & Kheddar, H. (2023). "Deep Learning-based Analysis of Algerian Dialect Dataset Targeted Hate Speech, Offensive Language and Cyberbullying." IJCDS, 13(1). * Due to the nature of this Dataset, comments contain offensiveness and hate speech. This does not reflect author values, however the aim is to providing a resource to help in detecting and preventing spread of such harmful content.
-
22
Talking Head Anime from a Single Image
Demo for the "Talking Head Anime from a Single Image"
Talking Head Anime from a Single Image is a machine learning project that demonstrates how neural networks can animate anime characters using only a single input image. The system generates animated facial expressions and movements by applying pose transformations to a static image of an anime character. The underlying model uses deep learning techniques to predict how different facial features and body parts should move based on pose parameters or input signals. This allows the software to create realistic animated frames while preserving the identity and appearance of the original character. The repository includes demo applications that allow users to interact with the system through graphical controls or webcam input to drive character motion. ... -
23
Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series forecasting framework. ... -
24
G2SConverter
Convert models from GoldSource engine to Source engine with AI
...To improve the quality of the texture, it is first Upscaled using RealESRGAN. The user can select scaling factor: x2, x4 or x8. After the Upscaling procedure, the texture is deblured using the Scale-recurrent Network for Deep Image Deblurring. An example of a processed texture is shown in the following image (parameters used: scaling-factor = 4 and deblur iterations = 4) besides upscaling and debluring the utility also generates normal maps for each texture. This is implemented using the DeepBump by HugoTiny model. Examples of normal maps are shown in the following images. -
25
StudioGAN
StudioGAN is a Pytorch library providing implementations of networks
...StudioGAN aims to offer an identical playground for modern GANs so that machine learning researchers can readily compare and analyze a new idea. Moreover, StudioGAN provides an unprecedented-scale benchmark for generative models. The benchmark includes results from GANs (BigGAN-Deep, StyleGAN-XL), auto-regressive models (MaskGIT, RQ-Transformer), and Diffusion models (LSGM++, CLD-SGM, ADM-G-U). StudioGAN is a self-contained library that provides 7 GAN architectures, 9 conditioning methods, 4 adversarial losses, 13 regularization modules, 6 augmentation modules, 8 evaluation metrics, and 5 evaluation backbones. ...
