Summary

In this chapter, we dove head-first into the world of ANNs. An ANN can be thought of as a stepwise non-linear approximation function that slowly adjusts itself to fit a curve that matches the desired input to the desired output. The learning process consists of several steps, including preparing data, labeling data, creating the network, initializing the weights, creating the forward pass that provides the output, and calculating the loss (also called the error). We created a special type of ANN, a CNN, to examine images. The network was trained using images with toys, to which we added bounding boxes to tell the network what part of the image was a toy. We trained the network to get an accuracy better than 87% in classifying images with toys in them. Finally, we tested the network to verify its output and tuned our results using the Adam adaptive descent algorithm.

In the next chapter, we will look at machine learning for the robot arm in terms of reinforcement learning...