Search Results for "bcftools-windows" - Page 5
Sort By:
Showing 635 open source projects for "bcftools-windows"
View related business solutions-
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.
-
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
1
Orion
A machine learning library for detecting anomalies in signals
Orion is a machine-learning library built for unsupervised time series anomaly detection. Such signals are generated by a wide variety of systems, few examples include telemetry data generated by satellites, signals from wind turbines, and even stock market price tickers. We built this to provide one place where users can find the latest and greatest in machine learning and deep learning world including our own innovations. Abstract away from the users the nitty-gritty about preprocessing,... -
2
Transformers
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX
Hugging Face Transformers provides APIs and tools to easily download and train state-of-the-art pre-trained models. Using pre-trained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities. Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages. Images, for tasks... -
3
Audiomentations
A Python library for audio data augmentation
A Python library for audio data augmentation. Inspired by albumentations. Useful for deep learning. Runs on CPU. Supports mono audio and multichannel audio. Can be integrated in training pipelines in e.g. Tensorflow/Keras or Pytorch. Has helped people get world-class results in Kaggle competitions. Is used by companies making next-generation audio products. Mix in another sound, e.g. a background noise. Useful if your original sound is clean and you want to simulate an environment where... -
4
Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase,... -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
-
5
gensim
Topic Modelling for Humans
Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora. The target audience is the natural language processing (NLP) and information retrieval (IR) community. -
6
Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that... -
7
MiniSom
MiniSom is a minimalistic implementation of the Self Organizing Maps
MiniSom is a minimalistic and Numpy-based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial Neural Network able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. Minisom is designed to allow researchers to easily build on top of it and to give students the ability to quickly grasp its details. The project initially aimed for a minimalistic implementation of... -
8
TFX
TFX is an end-to-end platform for deploying production ML pipelines
TensorFlow Extended (TFX) is a Google-production-scale machine learning platform based on TensorFlow. It provides a configuration framework to express ML pipelines consisting of TFX components. TFX pipelines can be orchestrated using Apache Airflow and Kubeflow Pipelines. Both the components themselves and the integrations with orchestration systems can be extended. TFX components interact with an ML Metadata backend that keeps a record of component runs, input and output artifacts, and... -
9
MNE-Python
Magnetoencephalography (MEG) and Electroencephalography EEG in Python
Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data. MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more. -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
-
10
omegaml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
omega|ml is the innovative Python-native MLOps platform that provides a scalable development and runtime environment for your Data Products. Works from laptop to cloud. -
11
Robyn
Experimental, AI/ML-powered and open sourced Marketing Mix Modeling
Robyn is an open-source, AI/ML-powered Marketing Mix Modeling (MMM) toolkit developed by Meta Marketing Science under the “facebookexperimental” GitHub umbrella. Its goal is to democratize rigorous MMM: what traditionally required expert statisticians and expensive consulting becomes accessible to any company with data. Robyn takes in historical data (spends on different marketing channels, conversions, or revenue, and optional context or organic-media variables) and uses a combination of... -
12
mlforecast
Scalable machine learning for time series forecasting
mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates... -
13
sktime
A unified framework for machine learning with time series
sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation, and forecasting. It comes with time series algorithms and scikit-learn compatible tools to build, tune and validate time series models. Our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides a... -
14
AIMET
AIMET is a library that provides advanced quantization and compression
Qualcomm Innovation Center (QuIC) is at the forefront of enabling low-power inference at the edge through its pioneering model-efficiency research. QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware... -
15
TabPFN
Foundation Model for Tabular Data
TabPFN is an open-source machine learning system that introduces a foundation model designed specifically for tabular data analysis. The model is based on transformer architectures and implements a prior-data fitted network that can perform supervised learning tasks such as classification and regression with minimal configuration. Unlike many traditional machine learning workflows that require extensive hyperparameter tuning and training cycles, TabPFN is pre-trained to perform inference... -
16
Watermark-Removal
Machine learning image inpainting task that removes watermarks
Watermark-Removal repository is a machine learning project focused on removing visible watermarks from digital images using deep learning and image inpainting techniques. The system analyzes an image containing a watermark and attempts to reconstruct the underlying visual content so that the watermark is removed while preserving the original appearance of the image. The project uses neural network models inspired by research in contextual attention and gated convolution, which are methods... -
17
Hamilton DAGWorks
Helps scientists define testable, modular, self-documenting dataflow
Hamilton is a lightweight Python library for directed acyclic graphs (DAGs) of data transformations. Your DAG is portable; it runs anywhere Python runs, whether it's a script, notebook, Airflow pipeline, FastAPI server, etc. Your DAG is expressive; Hamilton has extensive features to define and modify the execution of a DAG (e.g., data validation, experiment tracking, remote execution). To create a DAG, write regular Python functions that specify their dependencies with their parameters. As... -
18
Fairlearn
A Python package to assess and improve fairness of ML models
Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage. An AI system can behave unfairly for a variety of reasons. In Fairlearn, we define whether an AI system is behaving unfairly in terms... -
19
RF-DETR
RF-DETR is a real-time object detection and segmentation
RF-DETR is an open-source computer vision framework that implements a real-time object detection and instance segmentation model based on transformer architectures. Developed by Roboflow, the project builds upon modern vision transformer backbones such as DINOv2 to achieve strong accuracy while maintaining efficient inference speeds suitable for real-time applications. The model is designed to detect objects and segment them within images or video streams using a unified detection pipeline.... -
20
Chemprop
Message Passing Neural Networks for Molecule Property Prediction
Chemprop is a repository containing message-passing neural networks for molecular property prediction. -
21
stable-diffusion-videos
Create videos with Stable Diffusion
Create videos with Stable Diffusion by exploring the latent space and morphing between text prompts. Try it yourself in Colab. -
22
AutoMLOps
Build MLOps Pipelines in Minutes
AutoMLOps is a service that generates, provisions, and deploys CI/CD integrated MLOps pipelines, bridging the gap between Data Science and DevOps. AutoMLOps provides a repeatable process that dramatically reduces the time required to build MLOps pipelines. The service generates a containerized MLOps codebase, provides infrastructure-as-code to provision and maintain the underlying MLOps infra, and provides deployment functionalities to trigger and run MLOps pipelines. AutoMLOps gives... -
23
AtomAI
Deep and Machine Learning for Microscopy
AtomAI is a Pytorch-based package for deep and machine-learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by Maxim Ziatdinov at Oak Ridge National Lab. The purpose of the AtomAI is to provide an environment that bridges the instrument-specific libraries and general physical analysis by enabling the seamless... -
24
AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
AI-Engineer-Headquarters is a comprehensive educational repository designed to help developers become advanced AI engineers through a structured learning path and practical system-building exercises. The project serves as a curated collection of resources, methodologies, and tools covering topics across the entire artificial intelligence development lifecycle. Rather than focusing only on theoretical knowledge, the repository emphasizes applied learning and encourages engineers to build real... -
25
fklearn
Functional Machine Learning
fklearn uses functional programming principles to make it easier to solve real problems with Machine Learning.
