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Deep Learning with Python: Convolutional Neural Networks
Summary
Gain hands-on experience building, training, and evaluating convolutional neural networks (CNNs) using Python for image classification, object detection, and segmentation.
Explore the fascinating world of convolutional neural networks (CNNs) and uncover how they’ve revolutionized the field of computer vision and deep learning. Understand the building blocks of CNNs and delve into practical exercises using Python, focusing on real-world applications such as image classification, object detection, and image segmentation. Instructor Fred Nwanganga helps you develop proficiency in designing, implementing, and optimizing CNN models. Enhance your understanding of sophisticated architectures like VGG Net, ResNet, and EfficientNet, and discover how to apply pretrained models for enhanced accuracy and efficiency. Designed for data scientists, machine learning enthusiasts, and developers with a foundational understanding of deep learning and Python coding skills, this course helps you unlock advanced machine learning techniques. This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time, all while using a tool that you’ll likely encounter in the workplace. Check out “Using GitHub Codespaces" with this course to learn how to get started.