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| photo by ahmedgad from pixapay |
1- Introduction
The structure and operation of the human brain served as the inspiration for the class of machine learning algorithms known as neural networks. They are intended to identify intricate data patterns, learn from those patterns, and generate predictions based on what they have learned. Since they can handle large amounts of data and have the potential to be used in a variety of applications, neural networks have grown in popularity. The basic structure and operation of neural networks will be covered in this article's introduction.
2- What is a Neural Network?
A neural network is a collection of connected nodes, or "neurons," that are arranged in layers. The input layer receives data from the outside world, and the output layer produces the final result. In between the input and output layers are one or more hidden layers, where the actual learning takes place.
Each neuron in a neural network is connected to other neurons through "weights," which are essentially numerical values that determine the strength of the connection. These weights are adjusted during the learning process to improve the accuracy of the network's predictions.
3- How Neural Networks Work
By processing incoming data through layers of neurons to create an output, neural network’s function. The weights of the connections between the input layer and the first hidden layer are initially applied to the input data in the input layer.
The inputs from the input layer are then weighted summarized and sent to each neuron in the first hidden layer. The activation function runs this sum through to decide whether the neuron should "fire" and send its signal to the layer of neurons below it. After then, the weights of the connections between the first and second hidden layers are passed across the output of the first hidden layer, and so on, until the output layer generates the desired outcome.
4- Applications of Neural Networks
Neural networks have a broad spectrum of applications, from image and speech recognition to stock price prediction and medical diagnosis. They are especially helpful when there is a huge volume of data to evaluate and patterns are difficult to discover using standard approaches.
One application of neural networks is in natural language processing (NLP), where they are employed to comprehend and generate human language. Another use for neural networks is in self-driving automobiles, where they are used to analyze data from sensors and arrive at judgments based on that data.
5- Advancements in Neural Networks
Advances in neural networks have resulted in the creation of novel structures and methods for data processing that are more efficient and effective. Convolutional neural networks (CNNs), for example, are designed to process and evaluate images.
Another innovation is recurrent neural networks (RNNs), which can interpret data sequences such as time series or text. As a result, they are particularly useful in applications like speech recognition and natural language processing.
6- Conclusion
Neural networks are a strong type of machine learning algorithm that has grown in popularity in recent years. They can process massive volumes of data and have a wide range of applications, including picture and speech recognition, self-driving automobiles, and medical diagnosis. As neural network technology advances, we may expect to witness even more fascinating applications and discoveries in the next years.
References:
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning (Vol. 1). MIT Press.
- Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
- Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
- Ng, A. (2017). Neural Networks and Deep Learning. Coursera. https://www.coursera.org/learn/neural-networks-deep-learning