Overview

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

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