Last Updated on August 19, There are a lot of specialized terminology used when describing the data structures and algorithms used in the field.
In this post you will get a crash course in the terminology and processes used in the field of multi-layer perceptron artificial neural networks. After reading this post you will know:.
Discover how to develop deep learning models for a range of predictive modeling problems with just a few lines of code in my new book , with 18 step-by-step tutorials and 9 projects. We are going to cover a lot of ground very quickly in this post. The field of artificial neural networks is often just called neural networks or multi-layer perceptrons after perhaps the most useful type of neural network.
A perceptron is a single neuron model that was a precursor to larger neural networks. It is a field that investigates how simple models of biological brains can be used to solve difficult computational tasks like the predictive modeling tasks we see in machine learning.
The goal is not to create realistic models of the brain, but instead to develop robust algorithms and data structures that we can use to model difficult problems. The power of neural networks come from their ability to learn the representation in your training data and how to best relate it to the output variable that you want to predict.
In this sense neural networks learn a mapping. Mathematically, they are capable of learning any mapping function and have been proven to be a universal approximation algorithm.
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The predictive capability of neural networks comes from the hierarchical or multi-layered structure of the networks. The data structure can pick out learn to represent features at different scales or resolutions and combine them into higher-order features.
For example from lines, to collections of lines to shapes. These are simple computational units that have weighted input signals and produce an output signal using an activation function.
You may be familiar with linear regression, in which case the weights on the inputs are very much like the coefficients used in a regression equation.
Like linear regression, each neuron also has a bias which can be thought of as an input that always has the value 1. For example, a neuron may have two inputs in which case it requires three weights. One for each input and one for the bias. Weights are often initialized to small random values, such as values in the range 0 to 0. Like linear regression, larger weights indicate increased complexity and fragility.
It is desirable to keep weights in the network small and regularization techniques can be used.
Crash Course Overview
The weighted inputs are summed and passed through an activation function, sometimes called a transfer function. An activation function is a simple mapping of summed weighted input to the output of the neuron. It is called an activation function because it governs the threshold at which the neuron is activated and strength of the output signal. Historically simple step activation functions were used where if the summed input was above a threshold, for example 0.
Multilayer neural network pdf creator
Traditionally non-linear activation functions are used. This allows the network to combine the inputs in more complex ways and in turn provide a richer capability in the functions they can model.
A row of neurons is called a layer and one network can have multiple layers. The architecture of the neurons in the network is often called the network topology. The bottom layer that takes input from your dataset is called the visible layer, because it is the exposed part of the network.
1. Multi-Layer Perceptrons
Often a neural network is drawn with a visible layer with one neuron per input value or column in your dataset. These are not neurons as described above, but simply pass the input value though to the next layer. The simplest network structure is to have a single neuron in the hidden layer that directly outputs the value. Given increases in computing power and efficient libraries, very deep neural networks can be constructed.
Crash Course On Multi-Layer Perceptron Neural Networks
Deep learning can refer to having many hidden layers in your neural network. For example:. Data must be numerical, for example real values. This is where one new column is added for each class value two columns in the case of sex of male and female and a 0 or 1 is added for each row depending on the class value for that row. This same one hot encoding can be used on the output variable in classification problems with more than one class.
Neural networks require the input to be scaled in a consistent way. You can rescale it to the range between 0 and 1 called normalization.
Another popular technique is to standardize it so that the distribution of each column has the mean of zero and the standard deviation of 1. Scaling also applies to image pixel data. Data such as words can be converted to integers, such as the popularity rank of the word in the dataset and other encoding techniques.
Creating a Neural Network from Scratch in Python
The classical and still preferred training algorithm for neural networks is called stochastic gradient descent. This is where one row of data is exposed to the network at a time as input. The network processes the input upward activating neurons as it goes to finally produce an output value. This is called a forward pass on the network.
It is the type of pass that is also used after the network is trained in order to make predictions on new data. The output of the network is compared to the expected output and an error is calculated. This error is then propagated back through the network, one layer at a time, and the weights are updated according to the amount that they contributed to the error.
This clever bit of math is called the backpropagation algorithm. The process is repeated for all of the examples in your training data.
One of updating the network for the entire training dataset is called an epoch. A network may be trained for tens, hundreds or many thousands of epochs. The weights in the network can be updated from the errors calculated for each training example and this is called online learning. Alternatively, the errors can be saved up across all of the training examples and the network can be updated at the end.
This is called batch learning and is often more stable. Typically, because datasets are so large and because of computational efficiencies, the size of the batch, the number of examples the network is shown before an update is often reduced to a small number, such as tens or hundreds of examples. The amount that weights are updated is controlled by a configuration parameters called the learning rate.
It is also called the step size and controls the step or change made to network weight for a given error. Often small weight sizes are used such as 0. You can make predictions on test or validation data in order to estimate the skill of the model on unseen data. You can also deploy it operationally and use it to make predictions continuously. The network topology and the final set of weights is all that you need to save from the model.
Predictions are made by providing the input to the network and performing a forward-pass allowing it to generate an output that you can use as a prediction.
Do you have any questions about neural networks or about this post?
Ask your question in the comments and I will do my best to answer it. This is a nice article but there are some typos that need to be corrected.
Thank you for your time. Thanks for the article, fairly easy to understand. I agree with the typos though, they are in your statement about epochs and somewhere else. Great article! Kindly mention which are used in each case. I would say my deep learning training is back propagated with Stochastic Gradient Descent after reading this article. All my previous readings and training more clearer as I read this state of the art simple to comprehend article.
Thank you for this post Jason.
I have question please. Or is them two face for same coin?! Thank you :. Dear Jason, great post and very clear explanations! I have two questions regarding data input. Then you mention that features need to be scaled or standardized. Do you need to do this scaling on the one-hot encoded categorical features?
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Thanks, Dave. I started reading in blogs and scientific papers about deep learning and text classification methods. Name required. Email will not be published required.
Multilayer Neural Network in Tensorflow Python
Tweet Share Share. Model of a Simple Neuron. Model of a Simple Network. Anony-mus November 13, at am.
Jason Brownlee November 14, at am. Thanks, what typos? Viv December 8, at pm. Jason Brownlee December 9, at am.