In this video, we explore the fundamentals of Convolutional Neural Networks (CNNs), one of the most important architectures in deep learning and computer vision. This beginner-friendly tutorial explains how CNNs work, the types of layers they use, and their applications in image and video analysis.
🔍 What You’ll Learn
What is a Convolutional Neural Network (CNN/ConvNet)
How CNNs work: convolution, feature maps, and filters
Types of CNN layers: Convolutional, Pooling, Fully Connected, Dropout, and more
History and evolution of CNN architectures: LeNet-5, AlexNet, ZFNet, GoogleNet/Inception, VGGNet, ResNet
Applications in computer vision, image recognition, and deep learning
Whether you’re a beginner or looking for a refresher, this video provides a clear understanding of CNN concepts and architectures, helping you build a solid foundation in deep learning for computer vision.
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