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Learn to use Scipy for image processing in Python with DigitalSreeni. Master importing, flipping, and filtering images in under an hour.
Learn to manipulate and process images using Python's Pillow library with DigitalSreeni. Master resizing, cropping, and more in under an hour.
Learn to read images in Python using popular libraries in less than an hour with DigitalSreeni. Understand non-standard image reading and access the code on GitHub.
Learn Python's lists, tuples, dictionaries, and numpy arrays in under an hour with DigitalSreeni. Ideal for image processing.
Learn to denoise 2D and 3D multichannel scientific images using Noise2Void deep learning approach in less than an hour with DigitalSreeni.
Learn to denoise RGB images using deep learning with DigitalSreeni's concise guide. Master Noise2Void model training in under an hour. Accessible code included.
Learn the basics of data science, machine learning, and deep learning with DigitalSreeni in under an hour. Understand the differences and benefits, and structure your learning plan.
Learn to use the StarDist library for object detection of star-convex shapes in Python with DigitalSreeni. Less than 1-hour workload.
Learn to process whole slide images using openslide in less than an hour with DigitalSreeni. Extract, normalize, and save processed images separately. Code included.
Learn to label images for semantic segmentation using Label Studio in less than an hour with DigitalSreeni. Includes creating projects and exporting annotations.
Learn to emphasize edges in image processing using Python libraries with DigitalSreeni's tutorial. Covers Roberts, Sobel, Prewitt, and Canny filters. Less than 1 hour.
Learn to denoise MRI images using traditional methods like Gaussian smoothing, bilateral filtering, and more in less than an hour with DigitalSreeni.
Learn to adapt neural networks for specific applications using transfer learning in less than an hour with DigitalSreeni. Ideal for microscopy applications.
Learn to colorize black and white images using autoencoders in Python with DigitalSreeni's tutorial. Less than 1-hour workload.
Learn to build autoencoders in Python with DigitalSreeni. This under 1-hour tutorial covers basics, architecture, image importing, fitting, and results.
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