Introduction to Neural Networks for C#, Second Edition IX This book is dedicated to my neurons, without whose constant support this book would not have been. This book introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward. Introduction to Neural Networks with C#, Second Edition, introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural.
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Programming Neural Networks with Encog 3 in C# , PDF .. networks, reference “Introduction to Neural Networks with Java” and “Intro-. Apr 17, Introduction to Neural Networks for C#, 2nd Edition by Jeff Heaton Introduction to Neural Networks for C#, 2nd Edition pdf Introduction to Neural. Introduction. Welcome to the “An introduction to neural networks for beginners” book. This book consists of Part A of a much larger, forthcoming book – “From.
While processing a signal we'll call it a "pulse" , the signal will start at the top layer flowing through and being modified by each neuron in that layer. Diagram 3. Each neuron will modify the strength of the pulse. After the modification has been completed, the "pulse" will travel to the next layer and be modified again.
Diagram 4 Now that you have the details, let's take a step back and see how a large number of cells create a "neural net", or a network of neurons.
For a neural net to work, we need at least three groupings of neurons. Diagram 5 The top layer is used by the neural net to perceive the environment and is often called the "perception" or "input" layer.
This is where we will set initial values to be passed through the net with the pulse. Diagram 6 The bottom later is where the neural net will expose the final output of our pulse.
Notice these neurons don't send their signal anywhere. After the pulse travels to this layer, we'll go pick up the values on the output neurons as the final output of our network's processing.
Diagram 7. Last but not least, all the neurons in the middle layer s process the pulse as it travels through the net but are not exposed as direct input or output of the net.
You will be shown how to construct a Hopfield neural network and how to train it to recognize patterns. Chapter 4 introduces the concept of machine learning. To train a neural network, the weights and thresholds are adjusted until the network produces the desired output.
There are many different ways training can be accomplished.
This chapter introduces the different training methods. Chapter 5 introduces perhaps the most common neural network architecture, the feedforward backpropagation neural network. This type of neural network is the central focus of this book.
In this chapter, you will see how to construct a feedforward neural network and how to train it using backpropagation. Backpropagation may not always be the optimal training algorithm. Chapter 6 expands upon backpropagation by showing how to train a network using a genetic algorithm.
A genetic algorithm creates a population of neural networks and only allows the best networks to? Simulated annealing can also be a very effective means of training a feedforward neural network. Chapter 7 continues the discussion of training methods by introducing simulated annealing. Simulated annealing simulates the heating and cooling of a metal to produce an optimal solution.
Neural networks may contain unnecessary neurons. Chapter 8 explains how to prune a neural network to its optimal size. Pruning allows unnecessary neurons to be removed from the neural network without adversely affecting the error rate of the network.
In Figure 1-b, "male" is encoded as In addition to encoding non-numeric x-data, in many problems numeric x-data is normalized so that the magnitudes of the values are all roughly in the same range. In Figure 1-b, the age value of 35 is normalized to 3. The idea is that without normalization, x-variables that have values with very large magnitudes can dominate x-variables that have values with small magnitudes.
The heart of a neural network is represented by the central box. A typical neural network has three levels of nodes.
The input nodes hold the x-values. The hidden nodes and output nodes perform processing. In Figure 1-b, the output values are 0. These three values loosely represent the probability of conservative, liberal, and moderate respectively. Because the y-value associated with moderate is the highest, the neural network concludes that the year-old male has a political inclination that is moderate. The number of input and output nodes are determined by the structure of the problem data.
But the number of hidden nodes can vary and is typically found through trial and error. Each of these lines represents a numeric value, for example Also, each hidden and output node but not the input nodes has an additional special kind of weight, shown as a red line in the diagram.
These special weights are called biases. A neural network's output values are determined by the values of the inputs and the values of the weights and biases. So, the real question when using a neural network to make predictions is how to determine the values of the weights and biases.
This process is called training.
Put another way, training a neural network involves finding a set of values for the weights and biases so that when presented with training data, the computed outputs closely match the known, desired output values. Once the network has been trained, new data with unknown y- values can be presented and a prediction can be made. This book will show you how to create neural network systems from scratch using the C programming language. There are existing neural network applications you can use, so why bother creating your own?