Search Results for "linux text editor"

AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning,...

PipeRider automatically compares your data to highlight the difference in impacted downstream dbt models so you can merge your Pull Requests with confidence. PipeRider can profile your dbt models and obtain information such as basic data composition, quantiles, histograms, text length, top categories, and more. PipeRider can integrate with dbt metrics and present the time-series data of metrics in the report. PipeRider generates a static HTML report each time it runs, which can be viewed...

DATAGYM enables data scientists and machine learning experts to label images up to 10x faster. AI-assisted annotation tools reduce manual labeling effort, give you more time to finetune ML models and speed up your go to market of new products. Accelerate your computer vision projects by cutting down data preparation time up to 50%. A machine learning model is only as good as its training data. DATAGYM is an end-to-end workbench to create, annotate, manage, and export the right training data...

Code, run and monitor your data pipelines all from your browser! From idea to scheduled pipeline in hours, not days. Interactively build your data science pipelines in our visual pipeline editor. Versioned as a JSON file. Run scripts or Jupyter notebooks as steps in a pipeline. Python, R, Julia, JavaScript, and Bash are supported. Parameterize your pipelines and run them periodically on a cron schedule. Easily install language or system packages. Built on top of regular Docker container...

nonechucks is a library that provides wrappers for PyTorch's datasets, samplers and transforms to allow for dropping unwanted or invalid samples dynamically. What if you have a dataset of 1000s of images, out of which a few dozen images are unreadable because the image files are corrupted? Or what if your dataset is a folder full of scanned PDFs that you have to OCRize, and then run a language detector on the resulting text, because you want only the ones that are in English? Or maybe you...