36: Linear Regression | TensorFlow | Core API | Tutorial

The video discusses in TensorFlow core API: Linear regression

Dataset: Auto MPG

00:00:00 - Overview
00:01:00 - Import libraries
00:02:40 - Download data: Auto MPG
00:05:27 - Data: check missing values: .isna().sum()
00:06:57 - Data: convert dataframe to tensor
00:10:15 - Feature engineering: one-hot encoding
00:10:49 - Create function for one-hot encoding
00:14:58 - Data: One hot encode
00:17:09 - Normalize: create class Normalize(tf.Module)
00:19:54 - Normalize: x_train, y_train, x_test, y_test
00:21:42 - Build a machine learning model: create class LinearRegression(tf.Module)
00:25:24 - Check class LinearRegression()
00:26:24 - Predict (without training)
00:28:45 - Loss function: def mse_loss()
00:30:52 - Train and evaluate the model: set batch size
00:33:55 - Training loop: set parameters
00:35:29 - Training loop: begin
00:57:30 - Training loop: end
00:59:07 - Plot loss
01:01:22 - Saved model: create class ExportModule(tf.Module)
01:10:09 - Ending notes

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# TensorFlow Guide
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