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Learn to use Neural Networks for regression and compare them with other models in less than an hour with DigitalSreeni.
Learn to design deep neural networks with DigitalSreeni in under an hour. Includes data import, image plotting, model definition, and fitting. Code available on GitHub.
Learn data augmentation using Keras with DigitalSreeni in under an hour, covering reshaping, image and multiclass augmentation. Code included.
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.
Learn to build and deploy a Docker module using Python code on APEER with DigitalSreeni. Ideal for those with basic Python knowledge, this tutorial takes less than an hour.
Learn to classify malarial cells using CNN in Python with DigitalSreeni. In less than an hour, understand TensorFlow installation, network design, and testing.
Learn deep learning and neural networks in under an hour with DigitalSreeni's video tutorial, covering key terms and concepts with available code for practice.
Learn to implement Random Forest in Python with DigitalSreeni in under an hour. Includes data importing, sorting, and feature importance. Code available on GitHub.
Learn to perform Linear Regression using Sci-kit in Python with DigitalSreeni. In less than an hour, understand theory, model creation, and data prediction.
DigitalSreeni offers a beginner-friendly, under 1-hour introduction to machine learning, its types, and relevance to microscopy.
Learn data manipulation and analysis with Python's Pandas library in less than an hour with DigitalSreeni. Covers data loading and basic handling.
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