Keras is a high-level deep learning API designed to make neural network development easier and more productive. Today, it is most commonly used through TensorFlow as tf.keras.
Why Keras became popular
One reason Keras became popular is that it lets developers build useful models with a small amount of code. Instead of focusing on low-level tensor operations too early, you can focus on model structure, training, and evaluation.
Core concepts
- Layers: building blocks such as Dense, Conv2D, LSTM, Dropout
- Models: a stack or graph of layers
- Loss functions: how the model measures error
- Optimizers: how the model updates weights
- Metrics: what you track during training
A minimal example
from tensorflow import keras
from tensorflow.keras import layers
model = keras.Sequential([
layers.Dense(64, activation="relu", input_shape=(10,)),
layers.Dense(32, activation="relu"),
layers.Dense(1, activation="sigmoid")
])
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
What Keras is good for
- Rapid prototyping
- Educational deep learning projects
- Production training pipelines with TensorFlow
- Computer vision, tabular ML, NLP, and time-series work
Final thoughts
Keras lowers the barrier to entry for deep learning. It is not only beginner-friendly; it is also powerful enough for many real-world machine learning systems.
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