Learn KERAS with Real Code Examples

Keras allows developers to create deep learning models using Python with minimal boilerplate code.

It supports both sequential and functional model architectures for flexibility.

Used by researchers, hobbyists, and production teams for AI, computer vision, NLP, and reinforcement learning.

Layer-based model creation

Model compilation and training loops

Callbacks for monitoring and early stopping

Preprocessing utilities for data pipelines

Support for transfer learning and pre-trained models

Layer: fundamental computation unit

Model: container of layers

Loss function: guides optimization

Optimizer: adjusts model weights

Metric: evaluates model performance

main.py - entry point

data/ - datasets, preprocessing scripts

models/ - saved Keras models

utils/ - helper functions and callbacks

notebooks/ - experimentation and testing

Define model architecture (Sequential or Functional API)

Compile model with loss, optimizer, and metrics

Load and preprocess dataset

Train model using fit() with epochs and batch size

Evaluate model performance

Deploy or save trained model

Beginner: simple MLP for classification

Intermediate: CNN for image recognition

Advanced: RNN or Transformer for NLP

Expert: custom layers and multi-input/output models

Enterprise: production-ready pipelines and serving models

Keras vs PyTorch: high-level API vs dynamic computation graph

Keras vs TensorFlow: simpler interface vs full-feature control

Keras vs MXNet/Gloun: Python-focused vs multi-language support

Keras vs FastAI: abstraction for rapid prototyping vs layered high-level API

Keras vs HuggingFace Transformers: general neural nets vs specialized NLP models

2015 – Keras created by François Chollet

2016 – Adopted widely in research and industry

2017 – Integration with TensorFlow 2.0

2019 – Merged into TensorFlow core as tf.keras

2025 – Current version with modern TF 2.x features

Layer: basic building block

Model: container of layers

Optimizer: weight update algorithm

Loss function: guides learning

Callback: hooks into training lifecycle