Artificial Neural Networks (ANNs) are inspired by the human brain's structure and function. They consist of layers of interconnected nodes (neurons) that process information through weighted connections. Activation functions are crucial in ANNs as they introduce non-linearity, enabling the network to learn complex mappings between inputs and outputs. Without activation functions, ANNs would behave like linear regression models, limiting their ability to handle non-linear data. Activation functions transform input signals into output signals, which are then passed to the next layer. Common activation functions include binary step, linear, sigmoid, tanh, ReLU, Leaky ReLU, Parametrized ReLU, ELU, Swish, and Softmax. Each has unique properties and is suited for different tasks. For example, ReLU is widely used in hidden layers due to its simplicity and effectiveness, while Softmax is used for multi-class classification. Non-linear activation functions are essential for neural networks to model complex, high-dimensional data. They allow the network to adapt to changing input patterns and learn from data. The choice of activation function significantly impacts the network's performance, with factors such as the number of layers, training methods, and hyperparameters also playing a role. This paper discusses the importance of activation functions in deep learning, their types, and their roles in improving the performance of neural networks. It highlights the need for non-linearity in neural networks and the advantages of various activation functions. The paper also emphasizes the importance of selecting the right activation function for a given task, considering factors such as the type of problem, data characteristics, and desired output. While ReLU is commonly used, other functions like Leaky ReLU and ELU may be preferred in specific scenarios. The paper concludes that activation functions are vital for the effective functioning of neural networks and that further research is needed to explore their potential in improving model performance.Artificial Neural Networks (ANNs) are inspired by the human brain's structure and function. They consist of layers of interconnected nodes (neurons) that process information through weighted connections. Activation functions are crucial in ANNs as they introduce non-linearity, enabling the network to learn complex mappings between inputs and outputs. Without activation functions, ANNs would behave like linear regression models, limiting their ability to handle non-linear data. Activation functions transform input signals into output signals, which are then passed to the next layer. Common activation functions include binary step, linear, sigmoid, tanh, ReLU, Leaky ReLU, Parametrized ReLU, ELU, Swish, and Softmax. Each has unique properties and is suited for different tasks. For example, ReLU is widely used in hidden layers due to its simplicity and effectiveness, while Softmax is used for multi-class classification. Non-linear activation functions are essential for neural networks to model complex, high-dimensional data. They allow the network to adapt to changing input patterns and learn from data. The choice of activation function significantly impacts the network's performance, with factors such as the number of layers, training methods, and hyperparameters also playing a role. This paper discusses the importance of activation functions in deep learning, their types, and their roles in improving the performance of neural networks. It highlights the need for non-linearity in neural networks and the advantages of various activation functions. The paper also emphasizes the importance of selecting the right activation function for a given task, considering factors such as the type of problem, data characteristics, and desired output. While ReLU is commonly used, other functions like Leaky ReLU and ELU may be preferred in specific scenarios. The paper concludes that activation functions are vital for the effective functioning of neural networks and that further research is needed to explore their potential in improving model performance.