CNN Receptive Field | Deep Learning Animated

In this video, we explore the critical concept of the receptive field in convolutional neural networks (CNNs). Understanding the receptive field is essential for grasping how CNNs process images and detect patterns. We will explain both the theoretical and effective receptive fields, highlighting how they influence network performance and design. We start by defining the receptive field and its importance in CNNs, demonstrating how it grows with each convolutional layer. We'll use examples to compute the receptive field for different network configurations and show how pooling layers can significantly expand it. Finally, we'll delve into the differences between theoretical and effective receptive fields, providing insights on how CNNs utilize information during training. If you want to dive deeper into the topic of the receptive field, here are some references that you might find useful: - https://arxiv.org/abs/1701.04128 - https://theaisummer.com/receptive-field/ - https://distill.pub/2019/computing-receptive-fields/ Chapters: 00:00 Intro 01:10 Receptive Field Basics 03:00 Receptive Field Calculation 05:14 Example Network Analysis 06:12 Pooling Layers 07:18 Effective Receptive Field 10:15 Outro This video is animated using Manim, the Python animation library created by Grant Sanderson from @3blue1brown. Here is the code I used to make this video: https://github.com/ytdeepia/CNN-Receptive-Field Remember to like and subscribe to support the channel. #artificialintelligence #animation #deeplearning #ai #convolutionalneuralnetworks #receptivefield #python #tutorial