{"id":10766,"date":"2026-08-10T10:23:17","date_gmt":"2026-08-10T10:23:17","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/linkedin-deep-learning-with-python-optimizing-deep-learning-models-2025-2\/"},"modified":"2026-08-10T10:23:17","modified_gmt":"2026-08-10T10:23:17","slug":"linkedin-deep-learning-with-python-optimizing-deep-learning-models-2025-2","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/linkedin-deep-learning-with-python-optimizing-deep-learning-models-2025-2\/","title":{"rendered":"LinkedIn \u2013 Deep Learning with Python: Optimizing Deep Learning Models 2025-2"},"content":{"rendered":"
\n

Description<\/span><\/h2>\n

Deep Learning with Python: Optimizing Deep Learning Models. This course teaches techniques for improving the performance and efficiency of deep learning models. Taught by Frederik Nowangang, the course focuses on the practical aspects of machine learning and walks participants through real-world coding exercises. Key topics include data preprocessing, data augmentation, regularization methods to prevent overfitting, optimization algorithms, and hyperparameter tuning. The course is integrated with GitHub Codespaces, which provides a ready-made cloud development environment that allows for hands-on practice without the need for local setup. It includes all the essential tools and is accessible from any device, at any time. To learn how to use this environment, read the relevant section in the course. This course is designed for deep learning enthusiasts who want to improve their hands-on skills.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n