Search Results for "click"
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Showing 12 open source projects for "click"
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StemRoller
Isolate vocals, drums, bass, and other instrumental stems from songs
StemRoller is the first free app that enables you to separate vocal and instrumental stems from any song with a single click! StemRoller uses Facebook's state-of-the-art Demucs algorithm for demixing songs and integrates search results from YouTube. Simply type the name/artist of any song into the search bar and click the Split button that appears in the results! You'll need to wait several minutes for splitting to complete. Once stems have been extracted, you'll see an Open button next to the song - click that to access your stems! ... -
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Vearch
A distributed system for embedding-based vector retrieval
...Vearch implements a high-performance, lockless real-time vector indexing subsystem that utilizes various optimization techniques to support millisecond vector update and retrieval. End-to-end one-click deployment. Through the module of the plugin, a complete default visual search system can be deployed just with one click. Otherwise, you can easily customize your own image, video, or text feature extraction algorithm plugin. This GIF provides a clear demonstration of the project vearch usage and its internal structure. The use of vearch is mainly divided into three steps. ... -
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mlx
MLX: An array framework for Apple silicon
MlX offers a local web interface to browse, download, and run ML models via Hugging Face or local sources. It supports searching by tags or tasks, visualization of model metadata, quick inference demos, automatic setup of runtime environments, and works with PyTorch, TensorFlow, and ONNX. Ideal for researchers exploring and testing models via browser. -
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MNN
MNN is a blazing fast, lightweight deep learning framework
...In addition, MNN is also used on embedded devices, such as IoT. MNN Workbench could be downloaded from MNN's homepage, which provides pretrained models, visualized training tools, and one-click deployment of models to devices. Android platform, core so size is about 400KB, OpenCL so is about 400KB, Vulkan so is about 400KB. Supports hybrid computing on multiple devices. Currently supports CPU and GPU. -
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5
Metarank
A low code Machine Learning service that personalizes articles
...Ingest historical item listings, clicks and item metadata so Metarank can find hidden dependencies in the data using our simple JSON format.No Machine Learning experience is required, run our CLI tool with a set of features in a YAML configuration. Run Metarank API service, feed it with real-time events and receive a personalized ranking for your items that will boost conversion, click-through rate or any other business-critical metric you define. -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical... -
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DeepCTR
Package of deep-learning based CTR models
DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based... -
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SparrowRecSys
A Deep Learning Recommender System
...It includes components for offline data processing, feature engineering, model training, real-time data updates, and online recommendation services. SparrowRecSys supports a wide range of state-of-the-art recommendation algorithms, including models for click-through rate prediction and user behavior modeling that are widely used in advertising and content recommendation systems. The system is designed as a modular platform combining technologies such as Spark, TensorFlow, and web server components to represent the full lifecycle of recommendation pipelines. -
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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 machine learning systems used by digital advertising platforms. The repository categorizes papers by topic and provides links to research publications, allowing readers to easily explore the evolution of machine learning techniques in advertising and recommendation domains. ... -
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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Easy Machine Learning
Easy Machine Learning is a general-purpose dataflow-based system
Machine learning algorithms have become the key components in many big data applications. However, the full potential of machine learning is still far from being realized because using machine learning algorithms is hard, especially on distributed platforms such as Hadoop and Spark. The key barriers come from not only the implementation of the algorithms themselves but also the processing for applying them to real applications which often involve multiple steps and different algorithms. Our... -
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SPAWNN
SPatial Analysis With self-organizing Neural Networks
The SPAWNN toolkit is an innovative toolkit for spatial analysis with self-organizing neural networks which is particularily useful for spatial analysis, visualization and geographical data mining. To run the toolkit, simply download and execute (double-click) the jar-file. Please cite: - Hagenauer, J., & Helbich, M. (2016). SPAWNN: A Toolkit for SPatial Analysis With Self-Organizing Neural Networks. Transactions in GIS, 20(5), 755-775. Other related publications: - Hagenauer, J. (2016). Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks. ... -
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ArabicDiacritizer
An automatic restoration of Arabic diacritic marks
...If the following problem occured: <Access to the path '..\ArabicDiacritizer v1.0\text.data' is denied> - Access to the path "Program Files\ArabicDiacritizer\ArabicDiacritizer v1.0\", - Right click on "ArabicDiacritizer" - Choose "Run as administrator" For further information, please contact: rebai_ily
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