Скачать или смотреть What Causes Vanishing Gradients When Using Sigmoid Or Tanh? - AI and Machine Learning Explained

What Causes Vanishing Gradients When Using Sigmoid Or Tanh? Are you curious about why some neural networks struggle to learn effectively as they grow deeper? In this video, we’ll explain the reasons behind the vanishing gradient problem that occurs when using certain activation functions like sigmoid and tanh. We’ll start by describing how these functions behave mathematically and why their output saturates at extreme values. You’ll learn how this saturation causes derivatives to become very small, which in turn makes gradients diminish as they pass through multiple layers during training. We’ll discuss how this effect hampers the ability of early layers to update properly, leading to slow or stalled learning in deep neural networks. Additionally, we’ll compare sigmoid and tanh functions, highlighting their limitations and why modern neural network architectures often prefer alternatives like ReLU. Understanding the cause of vanishing gradients is essential for designing more efficient models that learn faster and perform better on complex tasks. Whether you’re a beginner or an experienced practitioner, grasping this concept will help you make informed choices about activation functions and network design. Join us to deepen your understanding of neural network training challenges and improve your AI development skills.

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