DATA SCIENCE

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The Covariance Matrix of data points is analyzed here to understand what dimensions(mostly)/ data points (sometimes) are more important.

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If you have used Numerical Analysis code in college, you can use them to fit curves in Machine Learning for very small datasets with low dimensions.

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The input the algorithm has taken is the number of clusters that are to be generated and the number of iterations in which it will try to converge clusters.

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Logistic Regression is trained using optimization methods like Gradient Descent or L-BFGS. NLP people will often use it with the name of Maximum Entropy Classifier.

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You can optimize the loss function using optimization methods like L-BFGS or even SGD. Another innovation in SVMs is the usage of kernels on data to feature engineers.

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These are basically multilayered Logistic Regression classifiers. FFNNs can be used for classification and unsupervised feature learning as autoencoders.

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Almost any state-of-the-art Vision-based Machine Learning result in the world today has been achieved using Convolutional Neural Networks.

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Pure RNNs are rarely used now but their counterparts like LSTMs and GRUs are state of the art in most sequence modeling tasks.

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CRF models each element of the sequence such that neighbors affect a label of a component in a sequence instead of all labels being independent of each other.

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Earlier versions like CART trees were once used for simple data, but with a bigger and larger datasets, the bias-variance tradeoff needs to be solved with better algorithms.