Showing 6 open source projects for "keygen activation code"

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  • 1
    fvcore

    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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  • 2
    latexify

    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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  • 3
    Neural Network Visualization

    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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  • 4
    Git Time Machine

    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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  • 5
    license4j

    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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  • 6
    DeepDream

    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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