Search Results for "activation" - Page 2
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Showing 43 open source projects for "activation"
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Arsen Clicker & Macro
The world’s smartest autoclicker & macro tool.
...Arsen allows precise control of mouse and keyboard actions, making it suitable for repetitive tasks, UI testing, data entry automation, and workflow optimization. The application includes a built-in macro editor for creating and managing complex automation sequences, along with global hotkey support for quick activation. Users can save multiple profiles and switch between them depending on their workflow. Arsen has Overlays which show FPS, CPS, CPU, MEM, Ping. Arsen is fully portable—no installation required—and runs entirely offline, ensuring that all macros and data remain secure on the user’s device. Arsen aims to deliver a lightweight, reliable, and streamlined automation tool that is easy to use yet powerful enough for advanced scenarios. ... -
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LibrePLM
LibrePLM integrates CAD editors in Odoo / LibrERP
...Server New Release : 2025 Q3 - Release on Odoo version 19.0. Client New Release : 2026 Q1 - Added Draftsight integration. Supported: Windows 11. Added new tool GetNodeID to help you asking activation code. Have more info : https://sourceforge.net/p/libreplm/wiki/Home Odoo is a product of Odoo SA world class leader in Open Source ERP system. Visit: http://www.odoo.com LibreERP and LibrePLM are products of Codebeex srl. Visit: http://www.codebeex.com Download Free Trial Client. Need support, customizations, more? ... -
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Yapsy
A fat-free DIY Python plugin management toolkit.
A simple framework for plugin system development with as few dependencies as possible. It is designed to offer a set of very lean classes (plugin managers and plugin interfaces) which can easily be customised by decoration or inheritance. Yapsy v1.x supports Python2 and Python3. Its source package contains versions of the sources for both pythons. Yapsy v2+ supports Python 3 and it's development happens now on https://github.com/tibonihoo/yapsy/ Usage samples, advices and... -
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Neural Network Visualization
Project for processing neural networks and rendering to gain insights
nn_vis is a minimalist visualization tool for neural networks written in Python using OpenGL and Pygame. It provides an interactive, graphical representation of how data flows through neural network layers, offering a unique educational experience for those new to deep learning or looking to explain it visually. By animating input, weights, activations, and outputs, the tool demystifies neural network operations and helps users intuitively grasp complex concepts. Its lightweight codebase is... -
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FairScale
PyTorch extensions for high performance and large scale training
...It introduced Fully Sharded Data Parallel (FSDP) style techniques that shard model parameters, gradients, and optimizer states across ranks to fit bigger models into the same memory budget. The library also provides pipeline parallelism, activation checkpointing, mixed precision, optimizer state sharding (OSS), and auto-wrapping policies that reduce boilerplate in complex distributed setups. Its components are modular, so teams can adopt just the sharding optimizer or the pipeline engine without rewriting their training loop. FairScale puts emphasis on correctness and debuggability, offering hook points, logging, and reference examples for common trainer patterns. ... -
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ConvNeXt
Code release for ConvNeXt model
ConvNeXt is a modernized convolutional neural network (CNN) architecture designed to rival Vision Transformers (ViTs) in accuracy and scalability while retaining the simplicity and efficiency of CNNs. It revisits classic ResNet-style backbones through the lens of transformer design trends—large kernel sizes, inverted bottlenecks, layer normalization, and GELU activations—to bridge the performance gap between convolutions and attention-based models. ConvNeXt’s clean, hierarchical structure... -
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LaMDA-pytorch
Open-source pre-training implementation of Google's LaMDA in PyTorch
Open-source pre-training implementation of Google's LaMDA research paper in PyTorch. The totally not sentient AI. This repository will cover the 2B parameter implementation of the pre-training architecture as that is likely what most can afford to train. You can review Google's latest blog post from 2022 which details LaMDA here. You can also view their previous blog post from 2021 on the model. -
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DeepLearning Tutorial
Deep Learning Tutorial, Excellent Articles, Deep Learning Tutorial
...The project is designed as a knowledge collection that helps beginners understand neural networks, deep learning architectures, and fundamental machine learning concepts. It contains curated learning materials covering topics such as feedforward neural networks, activation functions, backpropagation algorithms, optimization methods, and convolutional neural networks. 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. ... -
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kamiFaka
kamiFaka
Applicable to all kinds of e-commerce, coupons, forum invitation codes, recharge cards, activation codes, registration codes, Tencent iQiyi points CDK, etc., support manual and automatic delivery, and a tiered wholesale model similar to 1688. Stisla UI: The web interface is beautiful Front-end uses VUE3.0, millisecond-level response. Has integrated Alipay face-to-face payment, WeChat official, Payjs, Hupijiao, YunGouOS, Yipay, Mugglepay, V visa-free and other more than a dozen payment interfaces. ... -
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Text Gen
Almost state of art text generation library
...Something sweet built with Tensorflow and Pytorch(coming soon). Load your data, your data must be in a text format. Download the example data from the example folder. Tune your model to know the best optimizer, activation method to use. -
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Tensorflow 2017 Tutorials
Tensorflow tutorial from basic to hard
Tensorflow 2017 Tutorials is a structured set of tutorials that introduce developers to TensorFlow, starting with basic neural network constructs and progressing to sophisticated model architectures and training techniques. This repository covers essential building blocks like sessions (for older TF versions), placeholders, variables, activation functions, and optimizers, before guiding learners through building end-to-end models for regression, classification, and data pipelines. Beyond the basics, the project includes examples of convolutional neural networks, recurrent networks, autoencoders, reinforcement learning, generative adversarial networks, and transfer learning workflows. ... -
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Tensorflow and deep learning
A crash course in six episodes for software developers
...It is structured as a series of guided lessons that combine theoretical explanations, practical examples, and runnable code, allowing learners to build intuition while actively experimenting with models. The repository covers core neural network concepts such as weights, biases, activation functions, and gradient descent, as well as more advanced techniques like convolutional networks, recurrent networks, and reinforcement learning. It includes multiple hands-on projects, such as handwritten digit recognition, airplane detection in images, and text generation using recurrent neural networks, which demonstrate how different architectures solve real-world problems. -
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Exposure
Learning infinite-resolution image processing with GAN and RL
...ACM Transactions on Graphics (presented at SIGGRAPH 2018) Exposure is originally designed for RAW photos, which assumes 12+ bit color depth and linear "RGB" color space (or whatever we get after demosaicing). jpg and png images typically have only 8-bit color depth (except 16-bit pngs) and the lack of information (dynamic range/activation resolution) may lead to suboptimal results such as posterization. Moreover, jpg and most pngs assume an sRGB color space, which contains a roughly 1/2.2 Gamma correction, making the data distribution different from training images (which are linear). Exposure is just a prototype (proof-of-concept) of our latest research, and there are definitely a lot of engineering efforts required to make it suitable for a real product. -
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Neural Libs
Neural network library for developers
This project includes the implementation of a neural network MLP, RBF, SOM and Hopfield networks in several popular programming languages. The project also includes examples of the use of neural networks as function approximation and time series prediction. Includes a special program makes it easy to test neural network based on training data and the optimization of the network. -
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ThermV
Comprehensive thermal analysis software package
ThermV thermal analysis software package aims to provide the most sophisticated automatic analysis of thermal analysis data (TG/DTG, DTA and DSC). It offers new algorithm for concurrent peak deconvolution at different heating rates and provides full kinetic analysis of these data, including isoconversional methods for Ea, determination of reaction model and full kinetic triplet, Avrami coefficients, and dimensionality of crystal growth for reactions in the solid state. The project is... -
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KMG Kernel Module Configurator
A minimal python GUI that helps to manage modules in a linux system.
KMG helps you to manage: activation/disactivation of modules startup list blacklist and it has an unique feature: it can save a snapshot of the system's modules configuration and restore it later. All the actions are traced in a dedicated log section. The GUI also gives details on the selected module. -
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A firewall is used to control access to services (eg proxy) depending on the time of day, the day and the hosts the requests come from -- as the need may arise in a school or university. A web interface delegates control of activation to authorized users.
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remoteClam is a Windows-Service. It allows remote activation of virus scans with clamAV. It comes with a webinterface to allow easy clientscans.
