This guide focuses on using TensorFlow to train machine learning models, emphasizing basic classes over the tf.Keras API. It outlines steps involved in solving machine learning problems, including obtaining training data, defining a model, setting a loss function, and optimizing outputs. By creating a simple linear regression model, the guide illustrates the process of training with supervised learning, where inputs and outputs are tensors. Experimentation with synthesized data demonstrates how to effectively apply these steps for modeling in TensorFlow.
To develop a machine learning model, you must obtain training data, define a model, set a loss function, adjust variables with optimizers, and evaluate results.
In supervised learning, inputs and outputs are represented as tensors, learning to predict outputs based on pairs of values is essential for model training.
Collection
[
|
...
]