Artificial Neural Networks are a type of neural networks pretty much like the human brain. This kind of a system has the capability to perform complex mathematical computations and it usually consists of 3 layers, they are input mode, hidden mode...
Artificial Neural Networks are a type of neural networks pretty much like the human brain. This kind of a system has the capability to perform complex mathematical computations and it usually consists of 3 layers, they are input mode, hidden mode and the output mode. The input usually happens through nodes. The nodes are referred as neurons in case of artificial neural network. The first layer of an artificial neural network is referred as an input layer which is responsible for all the input data set, then this data set is further transferred towards the hidden layer. If a neural network contains more than one hidden layers then it is referred as a deep neural network. And the final layer then comes now is the output layer through which the whole data is processed. The main real world application of artificial neural network is to solve complex real world problems. Some of the best references for Artificial Neural Networks are Google Maps, Google Images and voice search algorithms used in platforms like Alexa. Google uses neural network capability to improve its listing in Google places and many other search queries.
An Artificial Neural Network has the following capabilities:
1 Feature Extraction: This feature is basically used in pattern matching and image recognition. 2 Categorization: In this case the ideas and the objects are recognized, understood and interpreted. 3 Association: This feature is used for pattern uncovering and matching the co-relations in the data set. 4 Optimization: This feature is basically used for analytical optimization for designing algorithms and writing proofs. 5 Generalization: This is a process of deploying the data the model that is completely trained into new data sets.
To build a machine learning algorithm, usually you’d define an architecture (e.g. Logistic regression, Support Vector Machine, Neural Network) and train it to learn parameters.
There has been hype about artificial intelligence, machine learning, and neural networks for quite a while now. This will not be a math-heavy introduction because I just want to build the idea here.
Artificial Neural Networks — Recurrent Neural Networks. Remembering the history and predicting the future with neural networks. A intuition behind Recurrent neural networks.
Artificial neural networks seen to be useful in many applications in recent times like prediction, classification, recognition,translation…
In this post, let's simplify and explain its working through examples that show how neural networks try to imitate and work similar to the way we think. Explaining the working of a neural network and keeping it as simple as possible by relating human thinking to the working of a neural network.