CNNs are deep learning architectures that learn directly from data. They are particularly effective for image recognition and pattern detection. CNNs can handle audio, time-series, and signal data classification
Deep learning enables machines to automatically learn high-level feature representations. Tutorial is suitable for beginners to intermediate readers. Deep learning is essential for data science and machine learning
CIFAR10 contains 60,000 color images across 10 classes. Each class has 6,000 images, divided into 50,000 training and 10,000 testing sets. Classes are mutually exclusive with no overlap
AI algorithm increases image size up to 8 times without quality loss. Tool works with images up to 16K resolution. Process takes 10-40 seconds to complete. Maximum file size is 30 MB
CNNs transform input images into class scores using learnable filters. CNNs use 3D volumes of neurons instead of fully connected layers. Input images are typically 32x32x3 pixels with RGB channels
AlexNet was created by Krizhevsky, Sutskever, and Hinton in 2012. Network contained 60 million parameters and 650,000 neurons. Achieved 15.3% top-5 error in ImageNet competition. Used non-saturating ReLU activation function