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What Keras does—and what a backend does
Keras provides the interface for defining, training, evaluating, and saving deep-learning models. It relies on a backend framework to perform the underlying computation. Keras 3 supports JAX, TensorFlow, and PyTorch as backends; the setup guide explains the current requirements and configuration options at Getting started with Keras.
For a first project, choose one backend rather than trying to learn all three at once. If you already use one of these frameworks, starting with it can simplify your environment and make it easier to follow related tutorials. There is no single backend established as best for every beginner or project; consider the framework your project depends on, the tutorial you plan to use, and compatibility with your environment.
Install Keras and configure a backend
Use a clean Python environment and follow the current Keras installation instructions. The documented PyPI command is pip install --upgrade keras, and Keras also requires a backend framework. Package compatibility details can change, so check the official setup page rather than combining install commands copied from older tutorials.
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Set the backend before importing Keras. For example, set KERAS_BACKEND to tensorflow, jax, or torch in your environment, then start Python and import Keras. The backend cannot be switched after Keras has been imported in that process.
Older instructions may describe a different Keras version: the Keras installation page says TensorFlow 2.16 and later install Keras 3 by default, while TensorFlow 2.15 installs Keras 2. It also documents tf_keras as the legacy package option. Check the live compatibility guidance for the package combination you intend to use.
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Build a first model with a complete example
A useful first exercise is the official Keras introduction for engineers, which trains a convolutional neural network to classify MNIST handwritten-digit images. It takes you through a complete task rather than stopping at a model definition, and the example is designed to work with JAX, TensorFlow, or PyTorch once you have selected a backend: Introduction to Keras for engineers.
As you work through it, focus on the sequence of tasks: prepare the data, define the model, train it, and evaluate its results. That sequence is more valuable at first than trying to understand every layer type or optimization choice. Run the notebook or code as written before changing the architecture; then adjust one part at a time so you can see how each change affects the workflow.
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If you prefer a hosted notebook, TensorFlow says its tutorials can run directly in Colab without local setup. Keras also notes that many of its guides are available as notebooks that run there. See TensorFlow tutorials and the Keras developer guides.
Choose a model-building API
Sequential: a straightforward stack
Start with the Sequential API when a model is a simple linear stack of layers, with one layer feeding into the next. TensorFlow’s beginner tutorials recommend beginning here because it offers a direct way to express that structure. Learn how to define the stack, compile the model, and use the training workflow before moving on to more flexible designs.
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Functional API: connected or branching models
Use the Functional API when a model needs a structure beyond a simple stack—for example, branches or multiple inputs and outputs. The Keras guide collection includes material on Functional models, along with examples that help show when this added flexibility is useful.
Subclassing and custom training
Model subclassing and custom training loops provide more control when built-in model structures or training workflows do not fit the task. They are valuable tools, but they add complexity; a beginner can usually defer them until a concrete model requirement calls for them. Browse the relevant topics in the Keras developer guides.
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What to learn after the first model
Build skills in the order your project needs them. The Keras guides and examples cover topics beyond a first classifier, including:
- Loading and preparing data, then evaluating a trained model.
- Saving models and understanding serialization.
- Using callbacks during training.
- Transfer learning and fine-tuning a model.
- Custom layers, distributed training, and custom training loops.
- Finding task-specific patterns in the Keras code examples.
You do not need to learn every advanced feature before building useful models. First become comfortable with the full training and evaluation cycle; then choose the next topic based on what your model or deployment setting requires.
Using Keras 3 with older Keras projects
Keras 3 supports multiple backends, but moving a project from Keras 2 may require code changes. The migration guide is particularly relevant to larger projects or code that relies on private or deprecated APIs. Consult Introducing Keras 3 before adapting an existing project, and test the migrated code rather than assuming that changing imports is sufficient.
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