How Does Backpropagation Work In CNNs? - AI and Machine Learning Explained

Описание: How Does Backpropagation Work In CNNs? Have you ever wondered how artificial intelligence systems learn to recognize images and improve over time? In this informative video, we'll explain the process behind how convolutional neural networks (CNNs) are trained to identify objects in pictures. We'll start by describing what happens during a forward pass, including how filters and activation functions work together to extract features like edges and textures from images. You'll learn about the role of pooling layers in making the process more efficient and how the network produces its predictions.

Next, we'll explore how the CNN compares its predictions to real labels using a loss function, and how this difference guides the learning process. We’ll then explain the concept of backpropagation, focusing on how the network adjusts its filters and weights through a method called gradient descent. This process involves calculating the contribution of each part of the network to the overall error, starting from the output layer and moving backward through the model.

Understanding this process is essential for grasping how AI models power applications like facial recognition, object detection, and even art creation tools such as DALL·E and Midjourney. Join us to learn how backpropagation helps AI systems improve their accuracy and reliability. Subscribe to our channel for more in-depth explanations about AI and machine learning.

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About Us: Welcome to AI and Machine Learning Explained, where we simplify the fascinating world of artificial intelligence and machine learning. Our channel covers a range of topics, including Artificial Intelligence Basics, Machine Learning Algorithms, Deep Learning Techniques, and Natural Language Processing. We also discuss Supervised vs. Unsupervised Learning, Neural Networks Explained, and the impact of AI in Business and Everyday Life.