Iam planning to do some literature work on rationale neural networks and functionalities of activation functions like sigmoid and others, please recommend me some effective articles related one
The best-suited activation function for real-time scenarios in neural networks depends on the specific task and the desired performance characteristics. However, some of the most commonly used and effective activation functions for real-time applications include:
The choice of activation function depends on various factors such as the type of neural network, the nature of the task, and the desired trade-off between accuracy and computational efficiency. It is often beneficial to experiment with different activation functions to find the one that performs best for a particular application.
I recommend Sigmoid because the output values between 0 and 1 make it suitable for binary classification problems. Best for the output layer in binary classification problems. They are also used in certain cases for hidden layers in specific scenarios.
A few articles for additional exploration:
1. "A Gentle Introduction to Neural Networks and Deep Learning" by Sebastian Raschka and Vahid Mirjalili. This article provides a beginner-friendly overview of neural networks, including an explanation of activation functions like sigmoid and their role in the network. It also covers the basics of deep learning and its applications.
2. "Understanding Activation Functions in Neural Networks" by Aakash N S. This article delves into various activation functions used in neural networks, including sigmoid, ReLU, and tanh. It discusses their properties, advantages, and disadvantages, helping you understand their functionalities and when to use them.
3. "On the Properties of Neural Networks: An Overview" by S. Haykin. This paper provides a comprehensive overview of neural networks, covering their foundational concepts, architectures, and learning algorithms. It explores activation functions and their impact on the behavior and performance of neural networks.
4. "Deep Sparse Rectifier Neural Networks" by Xavier Glorot, Antoine Bordes, and Yoshua Bengio. This research paper focuses on the rectified linear unit (ReLU), one of the widely used activation functions. It discusses the advantages of ReLU over sigmoid and other activation functions, highlighting its ability to mitigate the vanishing gradient problem.
5. "Universal Approximation Properties of Sigmoidal and Deep ReLU Networks" by Kurt Hornik. This paper investigates the universal approximation capabilities of sigmoidal and rectified linear unit (ReLU) neural networks. It discusses the expressive power of these activation functions and their ability to approximate a wide range of functions.
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