\( \tanh (1) \)
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Extra Insights
Did you know that the hyperbolic tangent function, \( \tanh(x) \), is defined as the ratio of the hyperbolic sine and hyperbolic cosine? So, \( \tanh(1) = \frac{\sinh(1)}{\cosh(1)} \). It gives you a value around 0.7616, which means that at \( x = 1 \), the function is positively slanted and behaves similarly to the sigmoid function, often used in neural networks. In practical terms, \( \tanh \) is used extensively in fields such as engineering and machine learning, particularly in activation functions for neural networks. Its range is between -1 and 1, making it a good fit for models that need to predict outputs that can vary in both directions. It's like being a trampoline that bounces values up and down while keeping them contained!