Скачать или смотреть What Is Gradient Descent In Neural Networks? - Tech Terms Explained

What Is Gradient Descent In Neural Networks? Have you ever wondered how neural networks learn and improve their predictions? In this informative video, we'll explain the core concept behind how machines get better at recognizing patterns and making decisions. We’ll start by describing what happens inside a neural network when it tries to make a prediction and how it measures its mistakes. You’ll learn about the process that guides the network to adjust its settings step by step, helping it become more accurate over time.

We’ll discuss the role of a key method called gradient descent, which acts as a guide to find the best settings for the network. You’ll see how the network uses calculus to figure out which way to move to reduce errors, and how the size of each step is controlled by a factor called the learning rate. Different approaches to this process, like batch, stochastic, and mini-batch methods, will also be explained to show how they help the network learn efficiently.

Understanding this process is essential for grasping how complex AI systems like image recognition, voice assistants, and language translation work behind the scenes. Join us for this clear and simple explanation, and subscribe to our channel for more insights into how digital technology functions and simplifies complex concepts.

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