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Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Artificial neural networks are computational systems vaguely inspired by design of natural neural networks (NNN). These systems are also called connectionist systems. Fundamental computational units are called nodes. These nodes represents neurons in natural neural networks. These nodes takes an input, perform computation on it and provides an output. Output of one node can be an input for another node. Nodes are connected with each other through edges. Each edge has a weight represented by a real number.

Weight is used to guide the direction of execution process. Usually these nodes stay in layers. First layer of artificial neural network is called input layer. Input layer is responsible to take input. Input layer is directly connected with hidden layers. These hidden layers perform computations on input. If a neural network has more than one hidden layers, then it’s called Deep Neural Network. Every node carries a weight. It is a real number.

A neural network is a system that learns how to make predictions by following these steps: Taking the input data. Making a prediction. Comparing the prediction to the desired output. Main Concepts. A neural network is a system that learns how to make predictions by following these steps: Taking the input data. Making a prediction. Comparing the prediction to the desired out

There are two ways to create a neural network in Python: From Scratch – this can be a good learning exercise, as it will teach you how neural networks work from the ground up Using a Neural Network Library – packages like Keras and TensorFlow simplify the building of neural networks by abstracting away the low-level code. If you’re already familiar with how neural networks work, this is the fastest and easiest way to create one. No matter which method you choose, working with a neural network to make a prediction is essentially the same: Import the libraries. For example import NumPy as np Define/create input data. For example, use NumPy to create a dataset and an array of data values.

Add weights and bias (if applicable) to input features. These are learnable parameters, meaning that they can be adjusted during training. Weights = input parameters that influence the output Bias = an extra threshold value added to the output Train the network against known, good data in order to find the correct values for the weights and biases. Test the Network against a set of test data to see how it performs. Fit the model with hyperparameters (parameters whose values are used to control the learning process), calculate accuracy, and make a prediction.