Search Results for "reference" - Page 11
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Showing 393 open source projects for "reference"
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python-tutorial
Practical Python tutorials, including Python basics
...It covers Python fundamentals, advanced language features, object-oriented programming, multithreading, databases, data science, Flask development, web crawling, and utility scripting. The project is intended for beginners learning Python and for working developers who want reference implementations for common scripts. Its examples are tested in a Python 3 environment and organized into topic-based directories. The repository also includes notebook-style learning materials for basic data types and core language concepts. It is useful as a structured self-study resource, classroom supplement, or quick reference for practical Python usage. -
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Deep-Learning-with-TensorFlow-book
Open source Deep Learning book, based on TensorFlow
Deep-Learning-with-TensorFlow-book is an open-source deep learning book based on TensorFlow 2.0. It combines theory with practical examples, making it suitable for beginners who want to learn deep learning through hands-on code. The repository includes the PDF book, companion source code, course slides, and notebook-style materials. It covers core machine learning and deep learning concepts through TensorFlow-based implementation. The project also supports educational use, with materials... -
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wpa-dictionary
WPA/WPA2 password dictionary for Wi-Fi password brute force cracking
wpa-dictionary is a Wi-Fi security wordlist repository focused on WPA and WPA2 password auditing. It collects several password dictionary files that can be used in authorized wireless security testing. The repository includes common password lists, English word material, birthday-pattern lists, and additional grouped text files. It also includes a small normalization script for preparing dictionary content. The README is written as a bilingual Chinese and English security-testing guide... -
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CleverHans
An adversarial example library for constructing attacks
...In versions v3.1.0 and prior, CleverHans supported TF1; the code for v3.1.0 can be found under cleverhans_v3.1.0/ or by checking out a prior Github release. The library focuses on providing a reference implementation of attacks against machine learning models to help with benchmarking models against adversarial examples. -
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TimeSformer
The official pytorch implementation of our paper
TimeSformer is a vision transformer architecture for video that extends the standard attention mechanism into spatiotemporal attention. The model alternates attention along spatial and temporal dimensions (or designs variants like divided attention) so that it can capture both appearance and motion cues in video. Because the attention is global across frames, TimeSformer can reason about dependencies across long time spans, not just local neighborhoods. The official implementation in PyTorch... -
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PyCls
Codebase for Image Classification Research, written in PyTorch
pycls is a focused PyTorch codebase for image classification research that emphasizes reproducibility and strong, transparent baselines. It popularized families like RegNet and supports classic architectures (ResNet, ResNeXt) with clean implementations and consistent training recipes. The repository includes highly tuned schedules, augmentations, and regularization settings that make it straightforward to match reported accuracy without guesswork. Distributed training and mixed precision are... -
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Linkedin Skill assessments
Full reference of LinkedIn answers 2023 for skill assessments
This repository is for those looking for answers to the LinkedIn assessment quiz questions or willing to help others by contributing to the tests. Or possibly you would like to create your first pull request and be added as a contributor. Add an explanation (or reference link) to your answers. That will help anyone to better learn concepts they might not have a great understanding of. Your resource for answers. In case you have doubts please contact this person or add them to review your PR. -
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Data Science Notes
Curated collection of data science learning materials
Data Science Notes is a large, curated collection of data science learning materials, with explanations, code snippets, and structured notes across the typical end-to-end workflow. It spans foundational math and statistics through data wrangling, visualization, machine learning, and practical project organization. The content emphasizes hands-on understanding by pairing narrative notes with runnable examples, making it useful for both self-study and classroom settings. Because it aggregates... -
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Awesome Graph Classification
Graph embedding, classification and representation learning papers
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations. -
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...By leveraging CNN architectures, the project showcases how learned embeddings can capture semantic similarity across varied images. This resource serves as both an educational reference and a foundation for further exploration in image retrieval research.
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Synonyms
Chinese synonyms, chat robot, intelligent question and answer toolkit
Chinese Synonyms for natural language processing and understanding. Better Chinese synonyms, chatbot, intelligent question and answer toolkit. synonymsCan be used for many tasks in natural language understanding, text alignment, recommendation algorithms, similarity calculation, semantic shifting, keyword extraction, concept extraction, automatic summarization, search engines, etc. Print synonyms in a friendly way for easy debugging. "Synonyms Cilin" was compiled by Mei Jiaju and others in... -
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Deep Exemplar-based Video Colorization
The source code of CVPR 2019 paper "Deep Exemplar-based Colorization"
...Experiments show our result is superior to the state-of-the-art methods both quantitatively and qualitatively. In order to colorize your own video, it requires to extract the video frames, and provide a reference image as an example. -
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Ad-papers
Papers on Computational Advertising
The Ad-papers repository is a curated collection of influential research papers focused on the fields of advertising technology, recommendation systems, and applied machine learning in online platforms. The repository organizes academic and industry papers that explore how machine learning algorithms can be used to improve ad targeting, user modeling, click-through rate prediction, and personalized recommendation systems. These papers represent key developments in large-scale industrial... -
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OpenAI Glow
Copy code in "Glow: Generative Flow with Invertible 1x1 Convolutions"
Glow is an open source generative model released by OpenAI that demonstrates flow-based generative modeling techniques. Unlike models that rely on approximate inference, Glow uses invertible transformations to directly learn the data distribution, allowing for exact likelihood computation and efficient sampling. The model is capable of producing high-quality synthetic images while maintaining interpretable latent spaces that enable meaningful manipulation of generated outputs. Glow’s... -
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Sparse Attention
"Generating Long Sequences with Sparse Transformers" examples
...By enabling tractable training on longer contexts, the project opened the door to applications in large-scale text and image generation. Though archived, it remains a key reference for efficient transformer research, influencing many later architectures that aim to extend sequence length while reducing compute. -
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The new version of MinimPy (MinimPy2) is available for download at: https://osdn.net/dl/minimpy2/MinimPy2.zip Please send your feedback about this new version to mahmood.saghaei@gmail.com (include MinimPy2 in the subject) ============================================================ MinimPy is a desktop application program for sequential allocation of subjects to treatment groups in clinical trials by using the method of minimization. Comprehensive reference help is available at http://minimpy.sourceforge.net MinimPy has been fully described in the following article: Saghaei, M. and Saghaei, S. (2011) Implementation of an open-source customizable minimization program for allocation of patients to parallel groups in clinical trials. Journal of Biomedical Science and Engineering, 4, 734-739. doi: 10.4236/jbise.2011.411090. ...
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interactive-coding-challenges
120+ interactive Python coding interview challenges
...Problems span arrays, strings, stacks, queues, linked lists, trees, graphs, dynamic programming, and more, mirroring common interview themes. Many challenges include hints and reference solutions so you can compare approaches and learn idiomatic patterns. The structure encourages incremental improvement—start with a brute-force idea, then refine to optimal time and space complexity. It serves both as a self-study path and as a warm-up bank for interview prep or coding katas. -
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DeepCluster
Deep Clustering for Unsupervised Learning of Visual Features
DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups. This alternating “cluster & train” scheme helps the model... -
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Ansible Examples
A few starter examples of ansible playbooks, to show features
...The examples highlight common Ansible practices such as organizing inventories, writing reusable playbooks, using roles, and handling variables and templates. They’re designed to be adapted directly into your own infrastructure or to serve as reference blueprints when learning how to structure automation projects. Whether you’re managing a handful of servers or deploying at scale, this repo provides starting points that illustrate how Ansible can streamline repetitive operational tasks. -
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Brand new cheatsheets and handouts
Matplotlib 3.1 cheat sheet
The Brand new cheatsheets and handouts repo is a compact, quick-reference summary of the most commonly used plotting commands and configurations in Matplotlib, intended to serve as a handy reference for experienced users who want to recall syntax or find the right function without digging into full documentation. It lays out common use cases (plot types, styling, figure configuration, saving/exporting, subplot layout, etc.) in a concise and organized format — often serving as a “cheat sheet” for rapid look-up. ... -
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pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
...Because the tutorials are concise and practical, the project is widely used in classrooms and self-study environments. Overall, it functions as both a learning curriculum and a quick reference for common PyTorch workflows. -
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PyText
A natural language modeling framework based on PyTorch
PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly... -
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SkyWater PDK
Open source process design kit for usage with SkyWater Technology
...Standard-cell libraries and IO pads are included so digital designers can use open synthesis and place-and-route to reach a manufacturable GDS. Because the PDK is open, it becomes a common target for community reference designs, open tapeouts, and teaching curricula. Documentation and example flows show how to assemble complete toolchains, from RTL to sign-off, using open EDA components. The project effectively lowers the barrier to custom silicon, catalyzing an ecosystem around an accessible 130-nm process. -
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PyTorch GAN Zoo
A mix of GAN implementations including progressive growing
...In addition to core GAN training, the repository includes tools for model evaluation, such as Inception Score and SWD metrics, as well as advanced features like GDPP for diverse generation and AC-GAN conditioning for class-specific synthesis. The framework also supports “inspirational generation,” enabling style or content transfer from reference images through pre-trained models. -
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micrograd
A tiny scalar-valued autograd engine and a neural net library
...It constructs a dynamic computation graph as you perform math operations and then computes gradients by walking that graph backward, making it an approachable “from scratch” autograd reference. On top of the core autograd “Value” concept, the project includes a small neural network library that lets you define and train simple models with a PyTorch-like feel, including multilayer perceptrons. The repository is intentionally compact and readable, prioritizing clarity over performance so learners can follow every step of gradient flow and parameter updates. ...
