Search Results for "malware-samples" - Page 5
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Showing 192 open source projects for "malware-samples"
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AutoClicker ArmoMan
AutoClicker, reach hight CPS
ArmoMan autoclicker is an app that permits you to reach Hight Click per Second by only holding a key of your choice. You can customize the speed and also the key to start the clicks. It can be used in video games but only on your own purpose. We will not be responsible for a ban. Discord: https://discord.gg/GWKMPJ74 Tutorial: https://youtu.be/cyAETJRwSaI GitHub: https://github.com/Armdevelopper099/autoclicker After the download you can go to https://cpstest.org/ to test the app... -
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lxspider
Educational Python web scraping case collection for many sites
lxSpider is a collection of web scraping examples designed primarily for learning and experimentation with data extraction techniques. It gathers numerous crawler implementations that demonstrate how to collect data from a wide range of websites and online services. It focuses heavily on practical cases that illustrate how different platforms handle requests, authentication parameters, and anti-scraping protections. lxSpider includes examples targeting areas such as e-commerce platforms,... -
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FusionCatcher
Somatic fusion-genes finder for RNA-seq data
FusionCatcher searches for novel/known somatic fusion genes, translocations, and chimeras in RNA-seq data (paired-end reads from Illumina NGS platforms like Solexa and HiSeq) from diseased samples. The aims of FusionCatcher are: - very good detection rate for finding candidate fusion genes, - very easy to use (i.e. no a priori knowledge of databases and bioinformatics is needed in order to run FusionCatcher), - very good detection of challenging fusion genes, like for example IGH fusions, CIC fusions, DUX4 fusions, CRLF2 fusions, TCF3 fusions, etc... -
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OpenAI Glow
Copy code in "Glow: Generative Flow with Invertible 1x1 Convolutions"
...Glow’s architecture is based on reversible layers and efficient flow operations, which allow large-scale training while keeping memory usage manageable. The repository provides training code, pretrained models, and scripts for generating samples or reproducing key results from the original research. Glow is primarily intended for researchers and practitioners exploring generative modeling, likelihood-based training, and interpretable deep learning systems. -
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Multilingual Speech Synthesis
An implementation of Tacotron 2 that supports multilingual experiments
This repository provides synthesized samples, training and evaluation data, source code, and parameters for the paper One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech. It contains an implementation of Tacotron 2 that supports multilingual experiments and that implements different approaches to encoder parameter sharing. It presents a model combining ideas from Learning to speak fluently in a foreign language: Multilingual speech synthesis and cross-language voice cloning, End-to-End Code-Switched TTS with Mix of Monolingual Recordings, and Contextual Parameter Generation for Universal Neural Machine Translation. ... -
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Reliable Metrics for Generative Models
Code base for the precision, recall, density, and coverage metrics
Reliable Fidelity and Diversity Metrics for Generative Models (ICML 2020). Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated images has been the Fréchet Inception Distance (FID) score. Because it does not differentiate the fidelity and diversity aspects of the generated images, recent papers have introduced variants of precision and recall metrics to diagnose those... -
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Machine Learning with TensorFlow
Accompanying source code for Machine Learning with TensorFlow
Machine Learning with TensorFlow is an open repository containing the source code and practical examples that accompany the book Machine Learning with TensorFlow. The project provides numerous code samples demonstrating how to build machine learning models using the TensorFlow framework. These examples illustrate core machine learning concepts such as regression, classification, clustering, and neural networks through practical implementations. The repository includes implementations of algorithms such as logistic regression, convolutional neural networks, and autoencoders, which allow readers to experiment with different learning techniques. ... -
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nonechucks
Deal with bad samples in your dataset dynamically
...Or maybe you have an AlternateIndexSampler, and you want to be able to move to dataset[6] after dataset[4] fails while attempting to load! PyTorch's data processing module expects you to rid your dataset of any unwanted or invalid samples before you feed them into its pipeline, and provides no easy way to define a "fallback policy" in case such samples are encountered during dataset iteration. -
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Resemblyzer
A python package to analyze and compare voices with deep learning
...The project is useful for researchers and developers who need a practical way to reason about speaker identity without building a voice encoder from scratch. It can help identify whether two recordings sound like the same speaker or visualize voice relationships across many samples. Its main value is making speaker representation accessible through a simple Python workflow. -
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GPT2-Pytorch with Text-Generator
Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation
...It uses pretrained weights converted for PyTorch rather than training the language model from scratch. Users can begin generation from a supplied prompt or request unconditional samples. Command-line options control sample count, batch size, output length, temperature, and top-k filtering. The repository includes a runnable Python script, dependency file, and Google Colab notebook for faster experimentation. It is best suited to studying an early Transformer language model and its sampling process rather than building a modern production system. -
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CakeChat
CakeChat: Emotional Generative Dialog System
CakeChat is a backend for chatbots that are able to express emotions via conversations. The code is flexible and allows to condition model's responses by an arbitrary categorical variable. For example, you can train your own persona-based neural conversational model or create an emotional chatting machine. Hierarchical Recurrent Encoder-Decoder (HRED) architecture for handling deep dialog context. Multilayer RNN with GRU cells. The first layer of the utterance-level encoder is always... -
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FID score for PyTorch
Compute FID scores with PyTorch
...FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. The weights and the model are exactly the same as in the official Tensorflow implementation, and were tested to give very similar results (e.g. .08 absolute error and 0.0009 relative error on LSUN, using ProGAN generated images). ... -
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Examples.Python
Shows how to use tkinter only by programming code (Python).
Examples Python shows how to use tkinter only by programming code (Python). * Github repository : https://github.com/gammasoft71/Examples.Python * Homepage : https://gammasoft71.wixsite.com/gammasoft/python -
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Dr0p1t-Framework
A framework that create an advanced stealthy dropper
Dr0p1t-Framework is a penetration testing tool designed to generate advanced and stealthy droppers capable of delivering and executing payloads on target systems while evading detection mechanisms. A dropper is a type of malware used to download and install additional malicious software, and this framework focuses on making that process more flexible and difficult to detect. It provides a wide range of modules that allow users to customize payload delivery, persistence mechanisms, and execution methods. The framework includes features such as antivirus evasion, privilege escalation, and system persistence, enabling it to maintain access on compromised systems. ... -
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Deepvoice3_pytorch
PyTorch implementation of convolutional neural networks
An open source implementation of Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning. -
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reNamer
Rename files depending on their .extension
If you want to rename your dataset samples for ML and you might have a lot of them (you should btw) or maybe you need to set different enumeration for every .extension you have or you just want to rename some personal stuff I am glad you are here. This is how you can rename your files: - For every .extension in the target folder reNamer sets unique enumeration. - Randomly - With your set of parameters. -
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more robust to noise and adversarial examples. This repository implements mixup for the CIFAR-10 dataset, showcasing its effectiveness in improving generalization, stability, and calibration of neural networks. The approach acts as a regularizer, encouraging linear behavior in the feature space between samples, which helps reduce overfitting and enhance performance on unseen data. -
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EfiPy
Python Library for accessing UEFI BIOS internal function by protocol
...EfiPy Shell package- Simple uefi shell program coded with EfiPy library to prove EfiPy workable EfiPy leverage these open source packages - ctypes, CorePy. Samples https://sourceforge.net/u/efipy/svn/HEAD/tree/Trunk/ EfiPy Author: https://www.linkedin.com/in/max-wu-a9068b90/ -
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tacotron
A TensorFlow Implementation of Tacotron
...Example training setups use LJ Speech, Nick Offerman audiobook recordings, and the World English Bible dataset. Users can monitor loss and attention plots during training to evaluate alignment quality. Pretrained checkpoints and generated samples are provided as references for reproducing or studying the model's behavior. -
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Siamese and triplet learning
Siamese and triplet networks with online triplet mining in PyTorch
...Such embeddings are commonly used in applications like face recognition, image similarity search, and few-shot learning. The repository demonstrates how to train these models using contrastive loss and triplet loss functions, which encourage embeddings of similar samples to be close while pushing dissimilar samples farther apart. It includes data loaders, training scripts, neural network architectures, and evaluation metrics that allow researchers to experiment with different embedding learning strategies. The project also implements online pair and triplet mining techniques to efficiently generate training examples during model training. -
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DC-TTS
TensorFlow Implementation of DC-TTS: yet another text-to-speech model
DC-TTS is a TensorFlow implementation of the DC-TTS architecture, a fully convolutional text-to-speech system designed to be efficiently trainable while producing natural speech. It follows the “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention” paper, but the author adapts and extends the design to make it practical for real experiments. The model is split into two networks: Text2Mel, which maps text to mel-spectrograms, and SSRN... -
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Vaex
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python
...Vaex is a high-performance Python library for lazy Out-of-Core data frames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted). Cut development cut development time by 80%. Your prototype is your solution. ... -
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GDeps
Automatic process to update and build your external libraries/projects
...Install: ========== 1) Install Python3: https://www.python.org/ 2) Install GDeps: https://sourceforge.net/projects/gdeps/files/latest/download?source=files 3) Download some samples projects: https://sourceforge.net/projects/gdeps/files/Projects/ 4) Configure your directories: https://sourceforge.net/projects/gdeps/files/Projects/directories.cfg/download 5) Configure your workspace: https://sourceforge.net/projects/gdeps/files/Projects/Config.cfg/download Links: ========== API: http://gdeps.org/doc/index.html Forum: http://gdeps.org/forum/index.php -
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PassGAN
A Deep Learning Approach for Password Guessing
...It reproduces ideas from the paper “PassGAN: A Deep Learning Approach for Password Guessing” using a modified Wasserstein GAN implementation. The repository includes scripts for training models and generating candidate password samples from learned distributions. A pretrained model based on the RockYou dataset is provided for reproducing experiments. TensorFlow and CUDA were used by the original implementation, reflecting the deep-learning tooling available when the project was created. The maintainer also documented experimental results and questioned how the approach compared with established RNN and Markov methods. ... -
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A Complete Beginner's Guide to Django
A Complete Beginner's Guide to Django - Code Samples
Code samples from the Django tutorial series. I’m starting a new tutorial series about Django fundamentals. It’s a complete beginner’s guide to start learning Django. The material is divided into seven parts. We’re going to explore all the basic concepts in great detail, from installation, and preparation of the development environment, models, views, templates, URLs to more advanced topics such as migrations, testing, and deployment.
