This paper discusses the importance and various types of activation functions in artificial neural networks (ANNs). Inspired by the human brain, ANNs consist of multiple layers of interconnected neurons that process information through synaptic connections. Activation functions play a crucial role in transforming input signals into output signals, enabling the network to learn and process non-linear mappings between inputs and outputs. The paper highlights that the choice of activation function significantly impacts the network's performance and accuracy. Key points include: 1. **Introduction to Activation Functions**: Activation functions are essential for transforming input signals into output signals, which are then fed to the next layer. The accuracy of neural networks depends on both the number of layers and the type of activation function used. 2. **Non-Linearity in Neural Networks**: Neural networks require non-linear activation functions to handle complex, non-linear data transformations. Linear activation functions are limited in their ability to learn and recognize complex patterns. 3. **Types of Activation Functions**: - **Binary Step Function**: Simple but limited in complexity. - **Sigmoid Function**: Commonly used for binary classification, but has issues with vanishing gradients. - **Linear Activation Function**: Directly proportional to the input, but lacks non-linearity. - **Tanh Function**: Symmetric around zero, with steeper gradients than sigmoid. - **ReLU Function**: Widely used for hidden layers due to its non-linearity and efficient gradient descent. - **Leaky ReLU**: Improves upon ReLU by addressing zero gradients for negative inputs. - **Parametrized ReLU**: Introduces a trainable slope parameter. - **Exponential Linear Unit (ELU)**: Uses a log curve for negative values. - **Swish Function**: Non-monotonic, outperforming ReLU in some cases. - **Softmax Function**: Used for multiclass classification, returning probabilities. 4. **Choosing the Right Activation Function**: The choice of activation function depends on the specific task and dataset. ReLU is generally preferred for hidden layers, while other functions like Leaky ReLU or Parametrized ReLU can be used for specific issues. 5. **Conclusion**: The paper emphasizes the importance of activation functions in improving the performance of deep learning models and highlights ongoing research in this area. The paper concludes by discussing the need for further research to compare and optimize various activation functions using standard datasets and architectures.This paper discusses the importance and various types of activation functions in artificial neural networks (ANNs). Inspired by the human brain, ANNs consist of multiple layers of interconnected neurons that process information through synaptic connections. Activation functions play a crucial role in transforming input signals into output signals, enabling the network to learn and process non-linear mappings between inputs and outputs. The paper highlights that the choice of activation function significantly impacts the network's performance and accuracy. Key points include: 1. **Introduction to Activation Functions**: Activation functions are essential for transforming input signals into output signals, which are then fed to the next layer. The accuracy of neural networks depends on both the number of layers and the type of activation function used. 2. **Non-Linearity in Neural Networks**: Neural networks require non-linear activation functions to handle complex, non-linear data transformations. Linear activation functions are limited in their ability to learn and recognize complex patterns. 3. **Types of Activation Functions**: - **Binary Step Function**: Simple but limited in complexity. - **Sigmoid Function**: Commonly used for binary classification, but has issues with vanishing gradients. - **Linear Activation Function**: Directly proportional to the input, but lacks non-linearity. - **Tanh Function**: Symmetric around zero, with steeper gradients than sigmoid. - **ReLU Function**: Widely used for hidden layers due to its non-linearity and efficient gradient descent. - **Leaky ReLU**: Improves upon ReLU by addressing zero gradients for negative inputs. - **Parametrized ReLU**: Introduces a trainable slope parameter. - **Exponential Linear Unit (ELU)**: Uses a log curve for negative values. - **Swish Function**: Non-monotonic, outperforming ReLU in some cases. - **Softmax Function**: Used for multiclass classification, returning probabilities. 4. **Choosing the Right Activation Function**: The choice of activation function depends on the specific task and dataset. ReLU is generally preferred for hidden layers, while other functions like Leaky ReLU or Parametrized ReLU can be used for specific issues. 5. **Conclusion**: The paper emphasizes the importance of activation functions in improving the performance of deep learning models and highlights ongoing research in this area. The paper concludes by discussing the need for further research to compare and optimize various activation functions using standard datasets and architectures.