Further reading
The following list will provide you with some additional reading that you may find useful in further understanding the materials in this chapter.
- A subset of the ImageNet dataset that is easier to use for experimentation: Tiny ImageNet: https://paperswithcode.com/dataset/tiny-imagenet
- This paper provides some interesting ideas on how to create an anomaly detection setup based on supervised methods: Toward Supervised Anomaly Detection: https://arxiv.org/ftp/arxiv/papers/1401/1401.6424.pdf
- This article provides additional information on the differences between MAD and STDEV: Relationship Between MAD and Standard Deviation for a Normally Distributed Random Variable: https://blog.arkieva.com/relationship-between-mad-standard-deviation/
- Understand the math behind PCA a little better: A Step-by-Step Explanation of Principal Component Analysis (PCA): https://builtin.com/data-science/step-step-explanation-principal-component-analysis
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