{"id":13153,"date":"2026-08-10T10:55:21","date_gmt":"2026-08-10T10:55:21","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-ai-deep-learning-from-scratch-in-python-2024-2\/"},"modified":"2026-08-10T10:55:21","modified_gmt":"2026-08-10T10:55:21","slug":"udemy-ai-deep-learning-from-scratch-in-python-2024-2","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-ai-deep-learning-from-scratch-in-python-2024-2\/","title":{"rendered":"Udemy \u2013 AI & Deep Learning From Scratch In Python 2024-2"},"content":{"rendered":"
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Description<\/span><\/h2>\n
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AI & Deep Learning From Scratch In Python Course. This course provides a comprehensive understanding of Convolutional Neural Networks (CNNs) by combining mathematical foundations and practical implementation in Python. Designed for beginners with no specific background, this course covers the fundamental concepts of Python programming and the computations required for deep learning. Each component of a convolutional neural network is first explained mathematically and then implemented step-by-step in Python. Through interactive programming exercises that can be run directly in the browser environment, participants gradually develop a complete object recognition framework based on an optimized CNN model. One of the class achievements of this course is an introduction to one of the most advanced and effective real-time multi-object recognition algorithms. Understanding the post-propagation process from both theoretical and practical aspects provides a solid foundation in the fundamental aspect of neural network training. By the end of the course, students will gain practical experience in building a complex CNN framework including advanced optimization and regularization techniques, which will enhance their ability to solve complex real-world object recognition problems with outstanding performance results and enhance their skills in the fields of computer vision and deep learning.<\/span><\/p>\n

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