{"id":11104,"date":"2026-08-10T10:27:48","date_gmt":"2026-08-10T10:27:48","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/linkedin-deep-learning-with-python-convolutional-neural-networks-2025-6\/"},"modified":"2026-08-10T10:27:48","modified_gmt":"2026-08-10T10:27:48","slug":"linkedin-deep-learning-with-python-convolutional-neural-networks-2025-6","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/linkedin-deep-learning-with-python-convolutional-neural-networks-2025-6\/","title":{"rendered":"LinkedIn \u2013 Deep Learning with Python: Convolutional Neural Networks 2025-6"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Deep Learning with Python: Convolutional Neural Networks. This course explores convolutional neural networks (CNNs) in deep learning with Python and teaches practical applications in machine vision, including image classification, object detection, and image segmentation. Throughout the course, you will learn the basic components of CNNs and, through hands-on exercises, you will learn how to design, implement, and optimize CNN models. Course instructor Fred Nwanganga will introduce you to advanced architectures such as VGG Net, ResNet, and EfficientNet, and show you how to use pre-trained models to improve accuracy and efficiency. This course is suitable for data scientists, developers, and machine learning enthusiasts who have an introductory understanding of deep learning and Python programming. The course is also integrated with GitHub Codespaces, which provides a ready-made cloud development environment and allows you to perform hands-on exercises without the need for a local setup. To learn how to use this tool, read the \u201cUsing GitHub Codespaces\u201d section in the course.<\/span><\/p>\n

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