01_machine_learning_for_trading
02_market_and_fundamental_data
06_machine_learning_process
10_bayesian_machine_learning
11_decision_trees_random_forests
12_gradient_boosting_machines
14_working_with_text_data
18_convolutional_neural_nets
20_autoencoders_for_conditional_risk_factors
21_gans_for_synthetic_time_series
22_deep_reinforcement_learning
Figure 18.1 - The result of basic edge filters applied to an image.png
Figure 18.10 - Ten types of land use contained in the dataset.png
Figure 18.11 - Cross-validation performance.png
Figure 18.12 - Cropped sample images of the SVHN dataset.png
Figure 18.13 - Cross-validation performance.png
Figure 18.14 - Information coefficient with respect to forward return by lag.png
Figure 18.15 - (Biased) out-of-sample information coefficients for best epochs.png
Figure 18.16 - Technical indicators.png
Figure 18.17 - Mutual information and two-dimensional grid layout for time series.png
Figure 18.18 - Dendrograms for row and column features.png
Figure 18.19 - Alphalens signal quality evaluation.png
Figure 18.2 - Typical operations in a two-dimensional convolutional layer.png
Figure 18.20 - Backtest performance in- and out-of-sample.png
Figure 18.3 - From convolutions to a feature map.png
Figure 18.4 - Predictive performance and computational complexity.png
Figure 18.5 - MNIST sample images.png
Figure 18.6 - Original and augmented samples.png
Figure 18.7 - Validation performance and test accuracy on CIFAR-10.png
Figure 18.8 - The VGG16 architecture.png
Figure 18.9 - Cross-validation performance accuracy and cross-entropy loss.png
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