Now that we've covered the fundamental ideas behind artificial neurons, network structures, and how they learn through supervised methods, the next logical step is to consider how we actually build and train these networks in practice. Manually implementing the mathematics of forward and backward propagation for anything beyond a trivial network quickly becomes complex and error-prone. This is where deep learning frameworks come into play, providing high-level building blocks and optimized operations. Among the most popular and user-friendly frameworks is Keras.

Keras is a high-level API (Application Programming Interface) for neural networks. Think of it as a specification or a user-friendly control panel for defining, training, and evaluating deep learning models. It focuses on developer experience, allowing you to build sophisticated models with relatively few lines of code. Keras was designed with guiding principles that make it particularly well-suited for both learning deep learning concepts and building real-world applications:

User Friendliness: Keras APIs are designed to be consistent, intuitive, and require minimal effort for common use cases. This significantly reduces cognitive load and speeds up development.
Modularity: A Keras model is typically constructed by connecting configurable building blocks (like layers, optimizers, activation functions) together. This makes it easy to experiment with different architectures.
Easy Extensibility: You can easily create custom building blocks (layers, loss functions, etc.) to support new research ideas or specific project needs.
Python Native: Keras models are defined in standard Python code, making them easy to integrate into existing Python workflows and leverage the rich Python data science ecosystem (NumPy, Scikit-learn, Pandas, etc.).

Keras and its Computational Backends

An important aspect to understand is that Keras itself doesn't perform the low-level tensor operations (like matrix multiplications and gradient calculations) directly. Instead, it acts as an interface that relies on an underlying backend engine for these optimized computations.

Historically, Keras was closely associated with TensorFlow. However, starting with Keras 3, the framework has become truly multi-backend. This means you can write your Keras code once and choose to run it on different optimized tensor libraries:

TensorFlow: A mature, widely-used framework developed by Google, known for its robust production deployment capabilities (TensorFlow Serving, TensorFlow Lite) and extensive ecosystem.
PyTorch: A popular framework developed by Meta AI, favored in the research community for its dynamic computation graphs and more "Pythonic" feel.
JAX: A high-performance library from Google for numerical computing and machine learning research, known for its function transformations (like automatic differentiation and vectorization).

Keras acts as a high-level interface, allowing you to choose an underlying backend (PyTorch, TensorFlow, or JAX) for numerical computation.

For this course, we will primarily use Keras 3 with the PyTorch backend. This choice reflects PyTorch's increasing adoption, particularly in research, and its often-praised developer experience. However, the beauty of Keras 3 is that most of the code you write will be portable across backends with minimal or no changes, giving you valuable flexibility.

Why Choose Keras?

Keras offers several advantages, especially when you are learning and building standard deep learning models:

Rapid Prototyping: Its simple, consistent API allows you to define and train models quickly, enabling fast iteration on ideas. Building a standard network often takes just a few lines of code, compared to potentially hundreds in lower-level frameworks.
Broad Compatibility: With Keras 3, your skills and code are more portable across different computational backends.
Built-in Support for Common Architectures: Keras provides pre-built layers and utilities for constructing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and other standard architectures, which we will cover in later chapters.
Large Community and Ecosystem: Being widely adopted means extensive documentation, tutorials, community support, and a wealth of pre-trained models available for use.

Keras in Your Workflow

Typically, using Keras involves these steps:

Define your model: You stack layers together to create your desired network architecture using either the Sequential or Functional API (covered in Chapter 2).
Compile your model: You configure the learning process by specifying an optimizer, a loss function, and evaluation metrics (covered in Chapter 3).
Train your model: You fit the model to your training data using the fit() method (covered in Chapter 3).
Evaluate your model: You assess the model's performance on unseen data (covered in Chapter 3 and 6).
Make predictions: You use the trained model to generate predictions on new data.

Keras provides clear, high-level functions for each of these steps, abstracting away much of the underlying complexity.

With this understanding of what Keras is and why it's a valuable tool for deep learning, the next step is to ensure you have it installed and ready to use. The following section will guide you through setting up your Python environment with TensorFlow (which includes Keras 3 by default) or setting up Keras with a PyTorch backend.