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New book mark!
‘this ain’t no hymn’: outline, chapter titles, and spoilers
Hey y’all. I’m still working on ‘this ain’t no hymn’ even though it’s been a while. I’ve decided to give y’all a revamped outline complete with chapter titles in order to tide y’all over until the next chapter is posted.
pass the happy! when you get this, reply with 5 things that make you happy & then send it to the last 10 people in your notifications 💛✨
5 things that make me happy:
Zhang Liao
Yue Jin
Yu Jin
Zhang He
Xu Huang
this ain’t no hymn
Hey remember my Bayo AU (https://celestial-vapidity.tumblr.com/post/634979690149543936/110-so-at-the-end-of-the-first-game-when)? Well I’m writing a fic of it.
Title: this ain’t no hymn
Summary: Bayonetta has no choice but to run. Jeanne has just broken free. Luka is just worried for his friend. OR an AU starting at the first game at the beginning of Chapter XVI: The Lumen Sage, and continuing through the entirety of the second game, in which mental illness exists and everyone is a lot gayer. I know these games are several years old, but don't read if you don't want spoilers for either game.
AO3: https://archiveofourown.org/works/30393069/chapters/74931018
FanFiction.Net: https://www.fanfiction.net/s/13852302/1/this-ain-t-no-hymn
Understanding the Tanh Activation Function: A Comprehensive Guide
Introduction:
The tanh activation function is a widely used mathematical function in the field of artificial neural networks. It plays a crucial role in transforming input values into output values within the desired range. In this blog post, we will explore the intricacies of the tanh activation function, its advantages, and how it can be leveraged to optimize machine learning models.
Understanding the Tanh Activation Function:
The tanh activation function, short for hyperbolic tangent, is a non-linear function that maps its input to a range between -1 and 1. It is symmetric around the origin, which means tanh(-x) = -tanh(x). This property makes it suitable for modeling symmetric data patterns.
Benefits of the Tanh Activation Function:
Non-linearity: The tanh function introduces non-linearity into the neural network, enabling it to learn and represent complex relationships between inputs and outputs.
Symmetry: The symmetric nature of the tanh activation function allows it to capture both positive and negative values effectively, making it ideal for tasks that involve balanced data distributions.
Smoothness: Unlike the step function, tanh provides smooth transitions between its outputs, facilitating more stable and continuous learning in neural networks.
Usage of Tanh Activation Function in Neural Networks:
The tanh activation function is commonly used in various parts of a neural network, including hidden layers and recurrent neural networks (RNNs). Its ability to handle both positive and negative values makes it suitable for modeling intricate data patterns and avoiding the vanishing gradient problem.
FAQs about the Tanh Activation Function:
Q: Is the tanh activation function suitable for all types of neural networks? A: While the tanh activation function can be effective in many scenarios, it may not be the best choice for networks with highly imbalanced data or networks that require outputs outside the -1 to 1 range.
Q: How does the tanh activation function differ from the sigmoid function? A: The main difference lies in the range of outputs. While the sigmoid function maps inputs to a range between 0 and 1, the tanh function maps inputs to a range between -1 and 1.
Conclusion:
In conclusion, the tanh activation function is a valuable tool in the realm of neural networks. Its non-linearity, symmetry, and smoothness make it an excellent choice for modeling complex data patterns. By understanding the strengths and limitations of the tanh activation function, you can leverage it effectively to enhance the performance of your machine learning models.
Remember, choosing the right activation function is crucial in building powerful neural networks. The tanh activation function offers unique advantages and should be considered when designing models for specific applications.
By incorporating the tanh activation function into your neural networks, you can unlock their full potential and achieve better accuracy and generalization in your machine learning tasks.
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Tanh the God of the gently Dead.
Coming soon the @instruomodular tanh[3] tanh[3] is a three channel, all analogue, waveshaper that takes an input signal and outputs the hyperbolic tangent function [tanh(x)] for that signal. In more useful terms, it adds curves to a signal! Traditionally the circuit is used to shape a triangle wave to a sine but when used with more complex signals it behaves as a single knob limiter. #BlickenSynths #Instruo #tanh[3] #waveshaper #ComingSoon #Eurorack #Modular #ModularSynthesizer #ModularSynthesizers #ModularSynth #ModularSynths #ModularSynthesis #Synthesizers #Synthesizer #Synths #Synth #SynthPorn #ElectronicMusic #ModularIsLife #UK #Webstore #InternetShopping #ShippingNow https://www.instagram.com/p/BwMWPCwFdDk/?utm_source=ig_tumblr_share&igshid=anozr0lp87r7