The book explains the inner workings of front and back propagation and aids in your comprehension of the fundamental concepts underlying neural network functioning. Utilize the Java environment to create apps for neural networks. This second edition demonstrates how to manually process your first neural network example once you have learned the rules governing neural network processing. You will understand how to process large-scale neural network applications using the Encog Java framework. The use of neural networks to approximate non-continuous functions is also discussed. Additionally, you will learn how to prepare the data for neural network growth, and you will be able to recommend various data preparation methods for a variety of unusual jobs. You will also get knowledge on subjects like network architecture, program code, programming logic, and execution that are related to the classification of handwritten numbers. The practical features of utilizing Java for neural network processing are covered in this book.
Artificial Neural Networks:
A particular kind of computational system called a neural network scales down the image of the human brain.
A branch of machine learning known as artificial neural networks (ANNs) is at the core of deep learning methods. Their design takes cues from the way organic neurons communicate with one another in the human brain.
Node layers, which comprise an input layer, one or more hidden layers, and an output layer, make up ANNs. Each node is interconnected and has a threshold and weight assigned to it. Data is forwarded to the network’s next layer when a specific node’s output rises above the designated threshold value. If not, no data is transferred to the network’s next tier.
Artificial neural networks are developed to simulate the human brain digitally. These networks can be used to create the next generation of computers because they are already being used for complicated analysis in a variety of disciplines, from engineering to medical. The gaming sector already heavily relies on artificial neural networks. They help us identify handwriting, which is helpful in fields like banking. In the area of medicine, artificial neural networks are also capable of many crucial things. They might be utilised to create human body models that would aid physicians in correctly diagnosing ailments in their patients. Furthermore, complicated medical pictures like CT scans may now be interpreted more rapidly and precisely thanks to artificial neural networks. Neural network-based machines will be able to figure out many abstract issues on their own. From their errors, they will grow. Maybe one day, a device known as a brain-computer interface will allow us to connect people to machines! This would translate mental cues from people into signals that robots could respond to. Maybe in the future, all of our interactions with the environment will be mental.
Topics include in this book:
- Chapter 1 Learning about Neural Networks is, where we explore about The architecture of an artificial intelligence neural network is conceptually similar to a human brain network. It is made up of layers of neurons that are linked in a specific orientation.
- Chapter 2 Internal Mechanics of Neural Network Processing. The inner workings of neural network processing are covered in this chapter. It demonstrates the creation, training, and testing of a network.
- Chapter 3 cover Manual Neural Network Processing. You will see a straightforward example of the internals of neural network processing in this chapter. I’ll go through each step of the mathematics needed to perform the forward and backward propagation passes in great detail.
- Chapter 4 is Configuring Your Development Environment. The subject of this book is Java-based neural network processing. You must become familiar with a number of Java tools before you can begin creating any neural network programmes. You can skip this chapter if you are a Java developer and are already familiar with the tools covered in it. Just make sure your Windows computer has all the programmes required installed.
- Chapter 5 is about Neural Network Development Using the Java Encog Framework. You will create your first basic programme using the function from Example 1 in Chapter 2 to help you understand how to create network programmes using Java.
- Neural Network Prediction Outside of the Training Range in Chapter 6. Usually, while working with neural networks, the most challenging and time-consuming activity is getting the data ready for processing.
- Processing Complex Periodic Functions is the next chapter 7. The discussion of how to process periodic functions is continued in this chapter, with a focus on more periodic functions.
- Approximating No continuous Functions in Chapter 8. The neural network approximation of no continuous functions will be covered in this chapter. Because neural network processing relies on calculating partial function derivatives (using the gradient descent algorithm), doing so for non-contiguous functions at the points where the function value abruptly jumps or drops produces ambiguous results, this area is currently problematic for neural networks.
- Approximating Continuous Functions with Complex Topology is the topic of Chapter 9. This chapter demonstrates how the approximation results of continuous functions with complex topologies are significantly enhanced by the micro-batch method.
- Using Neural Networks to Classify Objects, Chapter 10. You will classify items using a neural network in this chapter. Recognizing numerous objects and figuring out which class they fit into is what classification entails.
- Chapter 11 The Value of Choosing the Right Model. The example used in this chapter will ultimately result in a bad outcome. However, making blunders like these can teach you a lot.
- Approximation of Functions in 3D Space, the previous chapter 12. The topic of approximating functions in 3D space is covered in this chapter. Such function values are affected by two factors (instead of one variable, which was discussed in the preceding chapters). Everything covered in this chapter also applies to functions with more than two independent variables.