CoCalc -- Chapter

GitHub Repository: packtpublishing/machine-learning-for-algorithmic-trading-second-edition
Path: tree/master/figures/Chapter_13
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Name	Size	Last Modified
Figure 13.1 - Nonlinear dimensionality re duction.png	167.7 KB
Figure 13.10 - Return volatility patterns captured by the first two principal components.png	474.3 KB
Figure 13.11 - Eigenportfolio weights.png	113.8 KB
Figure 13.12 - Cumulative eigenportfolio returns.png	265.2 KB
Figure 13.13 - Silhouette plots for three and four clusters.png	66.6 KB
Figure 13.14 - Dendrograms and cophenetic correlation for different dissimilarity measures.png	164.8 KB
Figure 13.15 - Comparing the DBSCAN and HDBSCAN clustering algorithms.png	591.9 KB
Figure 13.16 - Original and clustered correlation matrix.png	828.7 KB
Figure 13.17 - Cumulative returns for the different portfolios.png	484.2 KB
Figure 13.2 - The number of features required to keep the average distance constant grows exponentially with the number of dimensions.png	20.7 KB
Figure 13.3 - Average distance of 1,000 data points in a unit hypercube.png	15.6 KB
Figure 13.4 - PCA in 2D from various perspectives.png	292.5 KB
Figure 13.5 - Visual representation of dimensionality reduction from 3D to 2D.png	80.3 KB
Figure 13.6 - The SVD decomposed.png	32.8 KB
Figure 13.7 - t-SNE and UMAP visualization of Fashion MNIST image data for different hyperparameters.png	980.2 KB
Figure 13.8 - (Cumulative) explained return variance by PCA-based risk factors.png	75 KB
Figure 13.9 - Explained variance of the top 10 principal components—100 trials.png	116.8 KB