In this article, I am going to explain how neurons learn from given data and used in prediction. We will inspect the theory behind the neural networks and training process, from A to Z. You will get the answers of the following questions in this article;
Neural Networks are in use widely in the scope of Artificial Intelligence. It can be a solution for most of the predictive problems thanks to its scalability and flexibility. You can solve complex problems on regression, classification, forecasting, object recognition, speech recognition, NLP and so on. So, what is Neural Networks, what makes them capable of all these problems and how does it learn to make predictions? In order to understand these, we need to know more about neurons and the math behind them. In this article, I am going to explain how neurons learn from given data and used in prediction. We will inspect the theory behind the neural networks and training process, from A to Z. You will get the answers of the following questions in this article;
Neurons are essential components of a Neural Network. More precisely, Neural Networks are formed by connecting one neuron to every other neuron. Unlike the human neural system, neurons in neural networks are connected to each other through the layers. Processing time would be extremely high if we consider that every neuron would be connected to every other neuron. In order to reduce processing time and save computer’s computational power, layers are used in the Neural Network. Thanks to layers, every neuron in a layer is connected to neurons in the next layer. The figure below represents the basic structure of a neural network with three layers.
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