The article provides an overview of various activation functions used in neural networks, highlighting their characteristics, advantages, and drawbacks. Key functions discussed include the sigmoid and hyperbolic tangent functions, which suffer from the vanishing gradient problem, making them less suitable for deep networks. The ReLU function, which addresses this issue, is commonly used despite its own drawback known as the dying ReLU problem, which can be mitigated by using the Leaky ReLU. The Exponential Linear Unit (ELU) and swish functions are presented as alternatives, with the latter showing improved performance in deeper networks but at the cost of increased computational complexity. The article emphasizes the importance of selecting an appropriate activation function to enhance the performance of neural networks and suggests exploring further resources for making informed choices.