From the course: Deep Learning with Python: Convolutional Neural Networks

CNNs - Python Tutorial

From the course: Deep Learning with Python: Convolutional Neural Networks

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CNNs

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- [Instructor] Convolutional neural networks are a specialized class of deep neural networks designed to handle data that has a grid-like structure, such as two-dimensional images. In practical terms, a CNN is a feedforward neural network that learns to extract features from data in a hierarchical manner. Early layers in a CNN detect simple patterns, such as edges, corners, or colors. As the data progresses through deeper layers, the network learns to recognize increasingly complex structures, including object, parts, and eventually complete objects. Although CNNs have been successfully applied to other types of data, such as audio signals and texts, they have achieved their greatest success in the field of computer vision. The design of CNNs is loosely inspired by how the human visual system works. A typical CNN for image recognition is organized into three main types of layers, each serving a distinct function. The first are the convolutional layers. These layers scan the input image with small filters to create feature maps that capture various visual patterns. Following the convolutional layers are the pooling layers. Pooling reduces the spatial dimensions of the data, lowering computational requirements, and enhancing the robustness of the extracted features. Finally, toward the end of the network, we have fully connected layers. These layers take the rich, high-level features extracted by the convolutional and pooling layers and use them to perform the final task, such as assigning a label to an image. We'll go into greater detail about each of these types of layers in the next two videos. Despite the emergence of new architectures, such as vision transformers, CNNs remain a critical tool in the field of computer vision. They are embedded in many technologies that are a part of our everyday life. Smartphones use CNNs for face detection and scene recognition. Social media platforms rely on CNNs to automatically identify and tag individuals in photos. Autonomous vehicles depend on CNNs to perceive their environment, identifying roads, pedestrians, and obstacles in real time. CNNs have transformed the field of computer vision, and their impact continues to grow as new applications emerge.

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