Keras is a powerful and user-friendly deep learning API, built on top of TensorFlow. It allows you to quickly prototype and build deep learning models with just a few lines of code.
Today, you'll learn how to use Keras to build, compile, and train neural networks. You'll apply your knowledge to a classic classification problem and understand key concepts like layers, optimizers, and callbacks.
- Keras Sequential API
- Dense and Dropout Layers
- Model Compilation and Training
- Loss Functions and Optimizers
- Model Evaluation and Callbacks
Exercise: Classify Digits with Keras (MNIST)
- Import the MNIST dataset using
tensorflow.keras.datasets. - Preprocess the data (normalize and one-hot encode labels).
- Build a
Sequentialmodel with at least one hidden layer. - Compile the model using
categorical_crossentropyand an optimizer likeadam. - Train the model and monitor performance using the validation set.
- Evaluate the model on the test set and visualize accuracy/loss curves.
Keras Beginner Tutorial with TensorFlow
Main resource for today
Keras Documentation
Official API and guides for building models
Hands-On Keras for Beginners
Detailed beginner's tutorial on Keras with visualizations
Understanding Callbacks in Keras
How to use callbacks like EarlyStopping and ModelCheckpoint
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