Book Image

Artificial Intelligence and Machine Learning Fundamentals

By : Zsolt Nagy
Book Image

Artificial Intelligence and Machine Learning Fundamentals

By: Zsolt Nagy

Overview of this book

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!
Table of Contents (10 chapters)
Artificial Intelligence and Machine Learning Fundamentals

Deep Learning

In this topic, we will increase the number of layers of the neural network. You may remember that we can add hidden layers to our graph. We will target improving the accuracy of our model by experimenting with hidden layers.

Adding Layers

Recall the diagram of neural networks with two hidden layers:

Figure 7.12: Diagram showing two hidden layers in a neural network

We can add a second layer to the equation by duplicating the weights and biases and making sure that the dimensions of the TensorFlow variables match. Note that in the first model, we transformed 784 features into 10 labels.

In this model, we will transform 784 features into a specified number of outputs. We will then take these outputs and transform them into 10 labels.

Determining the node count of the added hidden layer is not exactly science. We will use a count of 200 in this example, as it is somewhere between the feature and label dimensions.

As we have two layers, we will define two matrices (W1, W2) and vectors...