A minimal example of neural network, for the simple machine-learning task of linear regression Tensors and tensor operations Basic neural-network optimization
2.1 Example 1: Predicting the duration of a download using TensorFlow.js
2.1.1 Project Overview : Duration Prediction
2.1.2 A note on code listings and console interactions
2.1.3 Creating and formatting the data
2.1.4 Defining a simple model
2.1.5 Fitting the model to the training data
2.1.6 Using our trained model to make predictions
2.1.7 Summary of our first example
2.2 Inside Model.fit(): Dissecting gradient descent from Example 1
2.2.1 The intuitions behind gradient descent optimization
2.2.2 Backpropagation: Inside gradient descent
2.3 Linear regression with multiple input features
2.3.1 The Boston Housing Prices dataset
2.3.2 Getting and running the Boston-housing project from GitHub
2.3.3 Accessing the Boston-housing data
2.3.4 Precisely defining the Boston-housing problem
2.3.5 A slight diversion into data normalization
2.3.6 Linear regression on the Boston-housing data
2.4 How to interpret your model
2.4.1 Extracting meaning from learned weights
2.4.2 Extracting internal weights from the model
2.4.3 Caveats on interpretability
