Today, we dive into an integral piece of the deep learning puzzle: training your neural network. In this lesson, we will demystify what training entails and learn how to implement it using TensorFlow. By training the model, the neural network learns from the input data, gradually adjusting its parameters (weights and biases) to minimize the error in its predictions.

Training a neural network is akin to teaching a child to recognize shapes. The child learns from repeated exposure and feedback, just as a neural network learns from training datasets. In practice, the training process involves several rounds of forwarding input data through the network, calculating the error (the difference between the network's output and the actual, desired output), and adjusting the weights and biases to minimize this error. This process is much like a child adjusting their understanding of shapes based on feedback!

This iterative method allows the neural network to learn independently from the data and can eventually lead to accurate predictions or classifications, thereby enabling us to create powerful and predictive models.

The model.fit() method in TensorFlow is our main tool for training a neural network. This method takes in inputs and their corresponding target values, fitting the model to this data over a certain number of iterations known as epochs. Here are the key parameters we need to understand:

• X: Input data. This is the data from which your model will learn.