Introduction to Image Embedding and Accuracy

An Extensive Introduction to Autoencoders

With Clustering, Linear Discriminant Analysis, and Performance Evaluation

The previous chapter was a general introduction to Embedding, Similarity, and Clustering. This chapter builds upon these fundamentals by expanding the concept of embedding to include images as well. We will explore how K-Means clustering, introduced in the previous chapter, performs on image embeddings and introduce ways to measure performance through Accuracy and Recall. A simple autoencoder in the form …

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