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