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Machine learning has had a huge impact on the world over the last few decades, and its popularity seems to be ever-growing. Recently, more and more people have familiarised themselves with machine learning subfields, like neural networks, which are networks inspired by the human brain. In this article, Python code for a simple neural network that classifies 1x3 vectors with 10 as the first element, will be presented.

input_train = np.array([[0, 1, 0], [0, 1, 1], [0, 0, 0], [10, 0, 0], [10, 1, 1], [10, 0, 1]]) output_train = np.array([[0], [0], [0], [1], [1], [1]]) input_pred = np.array([1, 1, 0]) input_test = np.array([[1, 1, 1], [10, 0, 1], [0, 1, 10], [10, 1, 10], [0, 0, 0], [0, 1, 1]]) output_test = np.array([[0], [1], [0], [1], [0], [0]])

In the example, I have chosen a neural network with three input nodes, three nodes in the hidden layer, and one output node. The above __init__ function initializes variables describing the size of the neural network. inputSize is the number of input nodes, which should be equal to the number of features in our input data. outputSize is equal to the number of output nodes, and hiddenSize describes the number of nodes in the hidden layer. Further, there will be weights between the different nodes in our network that will be adjusted during training.

Many machine learning models are not able to understand the difference between e.g. units, and will naturally apply more weight to features of high magnitudes. This can destroy an algorithm’s ability to predict new data points well. Further, training machine learning models with features of high magnitude will be slower than necessary, at least if gradient descent is used. This is because gradient descent converges faster when the input values are in approximately the same range.

Neural networks are great at learning trends in both large and small data sets. However, data scientists have to be aware of the dangers of overfitting, which are more evident in projects where small data sets are used. Overfitting is when an algorithm is trained and modeled to fit a set of data points too closely so that it does not generalize well to new data points.

We will be using three packages for this project. NumPy will be used for creating vectors and matrices, as well as mathematical operations. Scikit-learn will be used for scaling the data, and Matplotlib will be used for plotting the error development during the training of the neural network.

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The purpose of the forward pass function is to iterate forward through the different layers of the neural network to predict output for that particular epoch. Then, looking at the difference between the predicted output and the actual output, the weights will be updated during backward propagation.

In addition to the variables describing the size of the neural network and its weights, I have created several variables that are initialized during the creation of a NeuralNetwork object that will be used for evaluation purposes. The error_list will contain the mean absolute error (MAE) for each of the epochs, and the limit will describe the boundary for when a vector should be classified as a vector with element 10 as the first element and not. Then, there are variables that will be used to store the number of true positives, false positives, true negatives, and false negatives.

scaler = MinMaxScaler() input_train_scaled = scaler.fit_transform(input_train) output_train_scaled = scaler.fit_transform(output_train) input_test_scaled = scaler.fit_transform(input_test) output_test_scaled = scaler.fit_transform(output_test)

Often, overfitting machine learning models have very high accuracy on the data sets they are trained on, but as a data scientist, the goal is usually to predict new data points as precisely as possible. To make sure that the model is evaluated based on how good it is to predict new data points, and not how well it is modeled to the current ones, it is common to split the datasets into one training set and one test set (and sometimes a validation set).

One of the easiest ways to get familiar with all the elements of a neural network is to create a neural network class. Such a class should include all the variables and functions that will be necessary for the neural network to work properly.

In our training and test data sets the values are in a relatively small range, and it might therefore not be necessary to do feature scaling. It is, however, included here so that people can use their own numbers without changing too much of the code. Doing feature scaling is extremely easy in Python due to the Scikit-learn package, and its MinMaxScaler class. Simply create a MinMaxScaler object, and use the fit_transform function with your non-scaled data as input, and the function will return the same data scaled. There are also other scaling functions in the Scikit-learn package that I encourage you to try.

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aving so many choices in Java, developers like to use only the popular libraries. All that the Spring Boot does is that it loads and configures them in the most standard way. Hence, the developers don’t need to spend a lot of time to configure up the same thing over and over again. In this way, they have more time for writing code and meeting business requirements.

In this simple neural network, we will classify 1x3 vectors with 10 as the first element. Input and output training and test sets are created using NumPy’s array function, and input_pred is created to test a prediction function that will be defined later. Both the training and the test data are comprised of six samples with three features each, and since the output is given, we understand that this is an example of supervised learning.