Master Convolutional Neural Networks (CNNs)—the backbone of modern computer vision and image recognition systems—with this hands-on, industry-focused course.
CNNs are widely used in applications such as facial recognition, object detection, medical imaging, and autonomous driving. This course provides both strong conceptual understanding and practical implementation skills required to build real-world AI solutions.
You will begin with an introduction to CNNs, understanding how they differ from traditional neural networks and why they are powerful for image data.
Next, you will explore convolutional layers and filters, learning how features like edges, textures, and patterns are extracted from images.
You will then study pooling layers and dimensionality reduction, which help improve performance and reduce computational complexity.
On the practical side, you will build CNN models using:
The course also covers regularization and data augmentation techniques, helping you improve model accuracy and prevent overfitting.
Finally, you will complete a hands-on CNN project, building an image classification model using the Fashion MNIST dataset, with performance evaluation and optimization.
