Training a Perceptron

To train a perceptron, we need the following components:

• Data representation
• Layers
• Neural network representation
• Loss function
• Optimizer
• Training loop

In the previous section, we covered most of the preceding components: the data representation of the input data and the true labels in TensorFlow. For layers, we have the linear layer and the activation functions, which we saw in the form of the net input function and the sigmoid function respectively. For the neural network representation, we made a function called perceptron(), which uses a linear layer and a sigmoid layer to perform predictions. What we did in the previous section using input data and initial weights and biases is called forward propagation. The actual neural network training involves two stages: forward propagation and backward propagation. We will explore them in detail in the next few steps. Let's look at the training process at a higher level:

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