Learn all about Keras

Learn all about Keras

Keras is an open-source neural network library that provides a high-level interface for building and training deep learning models. It is written in Python and is designed to be user-friendly, modular, and extensible. Keras was developed by François Chollet in 2015, and it has since become one of the most popular deep learning libraries in the world.

The book covers the following:

1. Introduction to Keras

What is Keras?

Keras vs. other deep learning frameworks

Why use Keras?

2. Setting Up Keras

Installation and requirements

Choosing a backend (TensorFlow, Theano, etc.)

Building your first Keras model

3. Data Preprocessing in Keras

Data preparation and cleaning

Feature engineering

Splitting data into train/validation/test sets

4. Keras Layers

Understanding different types of layers

Adding layers to your model

Customizing layers

5. Model Architecture

Sequential vs. Functional API

Designing a deep learning architecture

Tuning hyperparameters

6. Training and Evaluation

Setting up training parameters

Optimizers and loss functions

Monitoring training progress

Evaluating model performance

7. Advanced Keras Techniques

Transfer learning

Regularization techniques

Handling imbalanced datasets

Time-series forecasting

8. Deploying Keras Models

Converting Keras models to other formats (e.g. TensorFlow)

Deploying models in production

Building a REST API for your model

9. Keras and Beyond

Overview of other deep learning frameworks

Reinforcement learning with Keras

Cutting-edge research in deep learning