The article discusses the importance of understanding computer vision and building a Convolutional Neural Network (CNN) from scratch using Python library Numpy. It covers the main components of a CNN, such as convolutional layers and pooling layers, and provides Python implementations for these layers. The article also includes code examples and references for further learning.
Build a Convolutional Neural Network from Scratch using Numpy
Computer Vision is becoming increasingly important in our daily lives, and it’s crucial for Data Science practitioners to understand how it works. In this article, we’ll show you how to build a Convolutional Neural Network (CNN) using only the Python library Numpy.
CNNs are specifically designed for image-related tasks like image classification, object localization, and image segmentation. They mimic the human visual cortex, with each neuron responding to a small portion of the visual field. This makes CNNs highly effective for analyzing images.
Traditional fully connected networks struggle with large images due to the large number of parameters they require. CNNs solve this problem by implementing partially connected layers and weight sharing.
A CNN consists of two main components: convolutional layers and pooling layers.
Convolutional Layers
Convolutional layers apply filters to the input image, modifying it in specific ways. These filters highlight different features in the image, such as vertical or horizontal edges. The values of the filter elements are learned during training.
Pooling Layers
Pooling layers reduce the computational load and memory usage of the network by downsizing the input image. They aggregate sections of the image into a single value, typically using a max pooling kernel.
We have provided Python code examples for implementing the convolutional and pooling layers using Numpy. These examples demonstrate the forward propagation and backpropagation steps.
To get a practical understanding of CNNs, we invite you to clone our GitHub repository and experiment with the code. While our implementation may not achieve state-of-the-art performance, it still reaches 96% accuracy after a few epochs.
If you’re interested in expanding your knowledge of CNNs and computer vision, we recommend checking out the resources listed at the end of the article.
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