Ngesa Marvin - Keras (3) for the Curious and Creative | PyData Global 2023

Keras 3 is a high-level deep learning API that works with multiple backends (TensorFlow, JAX, PyTorch) through a common interface

Images are represented as matrices of numbers (pixels), ranging from 0-255 for grayscale or having RGB channels for color images

Key components of Keras models include:

• Layers (building blocks that encapsulate state and processing)
• Activation functions
• Loss functions
• Optimizers (like SGD, Adam, RMSprop)
• Data loaders and preprocessing

Three main ways to create Keras models:

• Sequential API (simple linear layer stacks)
• Functional API (complex architectures)
• Model subclassing (custom implementations)

Computer vision tasks possible with Keras include:

• Image classification
• Object detection
• Segmentation
• Super resolution
• Image generation

Modern features include support for:

• Diffusion models
• Stable diffusion for text-to-image generation
• Transfer learning with pre-trained models
• Multi-framework model deployment

Key concepts in training:

• Forward propagation passes inputs through the network
• Backward propagation calculates gradients
• Loss optimization adjusts weights to improve accuracy
• Batching improves training efficiency

Keras provides high-level abstractions while allowing low-level control when needed, making it suitable for both beginners and experts