Search Results for "activation code finder"
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29 projects for "activation code finder" with 1 filter applied:
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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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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.
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Claude Cognitive
Persistent context and multi-instance coordination
Claude Cognitive is an advanced memory and context-management extension designed to address the stateless limitations of Claude Code by giving the model a form of persistent “working memory” and multi-instance coordination. It introduces an attention-based context router that prioritizes files and content relevant to the current development discussion — tagging them as HOT, WARM, or COLD based on recency and keyword activation — so Claude Code doesn’t waste token budget rereading irrelevant code. -
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Flox
Developer environments you can take with you
...Instead of snowflake machines, teams define environments that layer or replace dependencies exactly where needed, then activate the same environment locally, in CI, or on production hosts. The GitHub repo and docs present a developer-first UX, plus integrations such as a VS Code extension that makes activating and working within Flox environments seamless. The ecosystem includes CI building blocks—for example, a CircleCI Orb—to install and activate environments as part of builds and tests. Flox’s messaging focuses on faster onboarding and “time-to-activation,” reducing friction for new contributors and multi-repo organizations. ... -
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fvcore
Collection of common code shared among different research projects
...It provides numerics and loss layers (e.g., focal loss, smooth-L1, IoU/GIoU) implemented for speed and clarity, along with initialization helpers and normalization layers for building PyTorch models. Its common modules include timers, logging, checkpoints, registry patterns, and configuration helpers that reduce boilerplate in research code. A standout capability is FLOP and activation counting, which analyzes arbitrary PyTorch graphs to report cost by operator and by module for precise profiling. The file I/O layer (PathManager) abstracts local/remote storage so the same code can read from disks, cloud buckets, or HTTP endpoints. Because it is small, stable, and well-tested, fvcore is frequently imported by projects like Detectron2 and PyTorchVideo to avoid duplicating infrastructure and to keep research repos. -
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TorchCode
Practice implementing softmax, attention, GPT-2 and more
...It is structured similarly to competitive programming platforms like LeetCode but focuses specifically on tensor operations and deep learning concepts. The platform provides a collection of curated problems that cover fundamental topics such as activation functions, normalization layers, attention mechanisms, and full transformer architectures. It runs in a Jupyter-based environment, allowing users to write, test, and debug their code interactively while receiving immediate feedback. An automated judging system evaluates correctness, gradient flow, and numerical stability, helping users understand both functional and theoretical aspects of their implementations. -
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latexify
A library to generate LaTeX expression from Python code
latexify_py converts small, math-heavy pieces of Python code into human-readable LaTeX that mirrors the intent of the computation, not just its surface syntax. It parses Python functions and expressions into an abstract syntax tree (AST), applies symbolic rewrites for common mathematical constructs, and then emits LaTeX that compiles cleanly in standard environments. Typical use cases include turning analytical utilities—like probability mass functions, activation formulas, or recurrence relations—into equations suitable for papers, notebooks, and slide decks. ... -
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Pika Skills
A collection of open-source skills for AI coding agents
Pika Skills is an open-source framework designed to extend the capabilities of AI coding agents by introducing modular, reusable “skills” that can be dynamically invoked during development workflows. Each skill acts as a self-contained unit composed of structured instructions, executable scripts, and dependency definitions, enabling agents to autonomously perform complex tasks without requiring manual configuration or orchestration. The system is tightly integrated with the Pika Developer... -
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Automated Interpretability
Code for Language models can explain neurons in language models paper
The automated-interpretability repository implements tools and pipelines for automatically generating, simulating, and scoring explanations of neuron (or latent feature) behavior in neural networks. Instead of relying purely on manual, ad hoc interpretability probing, this repo aims to scale interpretability by using algorithmic methods that produce candidate explanations and assess their quality. It includes a “neuron explainer” component that, given a target neuron or latent feature,... -
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Mixtral offloading
Run Mixtral-8x7B models in Colab or consumer desktops
...The project implements techniques that allow model components to be dynamically moved between CPU memory and GPU memory during inference, significantly reducing the amount of GPU VRAM required to run the model. This approach takes advantage of the sparse activation properties of mixture-of-experts architectures, where only a subset of expert networks are used for each token during generation. By selectively loading and caching the required experts, the system avoids keeping the entire model in GPU memory at once. The repository includes notebooks and code examples that demonstrate how to run large language models on consumer hardware such as personal GPUs or cloud notebook environments. -
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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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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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CAM
Class Activation Mapping
This repository implements Class Activation Mapping (CAM), a technique to expose the implicit attention of convolutional neural networks by generating heatmaps that highlight the most discriminative image regions influencing a network’s class prediction. The method involves modifying a CNN model slightly (e.g., using global average pooling before the final layer) to produce a weighted combination of feature maps as the class activation map. -
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Tensorflow 2017 Tutorials
Tensorflow tutorial from basic to hard
...By pairing code examples with conceptual explanations, the tutorials make abstract machine learning ideas accessible and encourage experimentation with TensorBoard visualization and distributed training. -
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Tensorflow and deep learning
A crash course in six episodes for software developers
Tensorflow and deep learning repository is an educational deep learning crash course designed to help software developers quickly understand and apply machine learning concepts without requiring advanced academic background. 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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wklaas
JMeasurement is a api for monitoring java production code
JMeasurement is a free and simple java api for monitoring runtime and usage (count, parallel activation, last activation) of user defined points in java production code. It is simple to use and extended. JMX is supported. Actual version is always in maven central. http://mvnrepository.com/artifact/net.sourceforge.jmeasurement2/JMeasurement -
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Git Time Machine
Atom package that allows you to travel back in commit history
git-time-machine is a user interface (often as an editor plugin or UI extension) that allows users to browse a file’s history visually, stepping back and forth through revisions in Git like a “time machine.” It shows changes to a file over time, lets users compare older states, and often provides diff and blame views to understand how the file evolved. Instead of just opening a commit log or diff, git-time-machine gives an interactive, incremental experience where you can slide through... -
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Mocha.jl
Deep Learning framework for Julia
Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels... -
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license4j
License4J Library and GUI Tools
LICENSE4J is a robust licensing library and license server that simplifies software licensing for developers. It allows for easy integration of licensing functionality into Java applications with minimal code. The user-friendly web-based License Manager works seamlessly on both desktop and mobile devices, enhancing accessibility for all users. The Licensing Library is a versatile tool that developers can easily integrate into any Java application. It empowers developers to implement... -
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GTK+ IOStream
GTK+iostream, Data plots, ORB, Neural Networks, WSOLA
...This is great for embedded memory saving code. You can also plot like so : Plot figure; figure.plot(x, y, x.size()); Plotting utilises GtkDataBox.sf.net ORB utilises www.zeroc.com Feed forward neural networks with different activation functions. Audio playback/recording and digital signal processing (DSP) - utilising Jack www.jackaudio.org -
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Import from github_com
Python module finder/loader from github, like in golang
import_from_github_com is a playful yet practical experiment that lets you import Python modules directly from GitHub using an import-like mechanism. The idea is to remove the friction of “clone → install → import” when you just want to try out a single file or small repo. It hooks into Python’s import machinery so that when you reference a GitHub path, it fetches the code, stores it locally (often with caching), and makes it importable in the current environment. This is helpful for... -
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DeepDream
This repository contains IPython Notebook with sample code
...It walks through loading a pretrained network, selecting layers and channels to maximize, computing gradients with respect to the input image, and applying multi-scale “octave” processing to reveal fine and coarse patterns. The code is intentionally compact and exploratory, encouraging users to tweak layers, step sizes, and scales to influence the aesthetic. Although minimal, it illustrates important concepts like feature visualization, activation maximization, and the effect of different receptive fields on the final image. -
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Relation Tags
Source code for be able to use Relation Tags.
Source code for be able to use Relation Tags. It is part of project VocabularyMem but can be used separately. Relation Tags are tags which can be relationed together . For example tag "Paris" and tag "France" can be relationed with a relation "is part of". This code is created from 0 and is able to define which type of relation we use, using most elemental mathematic properties. It is strongly recommended to read "Relation Tags guide for programmers". Inside source zip, also contains dialogs... -
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The Mind Module
Gives agents personality, moods, emotions and emotional attachments.
...In the folder MMUnity (see Files) you will find an implementation of the MindModule for Unity by Henrik Warpefelt and Staffan Widegarn Åhlvik. The original C++ code by Mirjam P Eladhari is currently being brought into the 21st century and will appear here in the near future. Drop us an email if you wish to access the code as is. -
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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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This Java software is useful to set id3v1 and id3v2 tags on mp3 files, possibly in a massive way. It helps you managing a song database, searching for duplicate songs. It has a rename file utility, not only for mp3 files. It has an utility panel to create playlists, re-organize files in folders with the name format you prefer, an utility to optimize the CD disk space. This program was written in 2002, so I don't remember much about it ... but I used it a lot, as much as my close...
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qlsdup
GUI based lightweight duplicate file finder / remover.
Simple GUI based lightweight duplicate file finder and remover. Configurable file search and preview (via external tools) option for file data. No command-line interface available! This is basically a reimplementation of dupfinder as: it doesn't compile on my computer, the executable won't work either, the projects seems dead and I didn't wanna work through the original code.
