Search Results for "code activation automesher"
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10 projects for "code activation automesher" with 2 filters applied:
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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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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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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... -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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5
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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10
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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