We present our first neural network, which learns how to map training examples (input array) to targets (output array). Let's assume that we work for one of the largest online companies, Wondermovies, which serves videos on demand. Our training dataset contains a feature that represents the average hours spent by users watching movies on the platform and we would like to predict how much time each user would spend on the platform in the coming week. It's just an imaginary use case, don't think too much about it. Some of the high-level activities for building such a solution are as follows:
- Data preparation: The get_data function prepares the tensors (arrays) containing input and output data
- Creating learnable parameters: The get_weights function provides us with tensors containing random values that we will optimize to solve our problem...
