Learn KERAS with Real Code Examples

Sequential API -> stack layers linearly

Functional API -> define complex DAG models

Model class -> central abstraction for training and evaluation

Layers -> building blocks of neural networks

Callbacks -> event-driven extensions for training lifecycle

N/A - computational graphs for deep learning

Layer stacking and chaining

Automatic differentiation

GPU/TPU accelerated computation

Support for sequential and DAG architectures

Sequential models for linear stacks

Functional API for DAG and multi-input/output

Callback-driven training lifecycle

Layer-based modular abstraction

Integration with dataset pipelines

Image classification networks (CNNs)

Sequence models (RNN, LSTM, Transformers)

Time series forecasting

Reinforcement learning agents

Multi-modal learning tasks

User-friendly API

Modularity and extensibility

Backend-agnostic design

Supports rapid prototyping

Integration with TensorFlow ecosystem

Use GPUs/TPUs for large models

Optimize batch sizes

Leverage data generators

Use distributed training if needed

Profile memory and computation performance

Upgrade code to TensorFlow 2.x if using older Keras

Replace deprecated APIs

Check compatibility with custom layers

Update dataset pipelines as needed

Validate trained models on new backend versions