TensorFlow is a machine learning framework used to build and train models.

At a very basic level, you can think of it as a toolkit for:

• working with tensors, which are multi-dimensional arrays
• building neural networks
• training models on data
• running predictions on new inputs

Common beginner ideas:

• tf.Tensor: the basic data structure
• keras.Sequential: a simple way to define a model layer by layer
• model.fit(...): trains a model
• model.predict(...): makes predictions

Very small mental model:

1. Load data.
2. Define a model.
3. Train the model.
4. Evaluate the result.
5. Use the model for prediction.

Simple example:

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(8, activation="relu"),
    tf.keras.layers.Dense(1)
])

model.compile(optimizer="adam", loss="mse")

Why people use TensorFlow:

• good ecosystem
• strong support for deep learning
• useful for production deployment
• integrates well with Keras

Good first goal:

Build a tiny model that learns from a small table of numbers before moving into images, text, or time series.