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It is a technique used in machine learning and data science to reduce the number of input variables (features) in a dataset while retaining as much meaningful information as possible. It is essential when dealing with high-dimensional data (i.e., when there are many features) to improve computational efficiency, reduce overfitting, and visualize data more easily. a) Image data set Colab Link: https://colab.research.google.com/drive/1IVwTCiaOXS87xwHSCdG1tbBNZ1fyoIxa#scrollTo=8Ojlz34ENO0I b) Tabular data set Colab Link: https://colab.research.google.com/drive/14Dt3Kyuo_3HQ_hMjC5sX_ORTwixLp9p9#scrollTo=WlS0w3p7oTwK c) Datbricks Colab Link : https://colab.research.google.com/drive/1X0jWRiOoJpiK4KiGW6JAtACdjSL-gmDv Youtube link: https://youtu.be/ilbMDqOuZX8

There was an error while loading. Please reload this page. This project has not set up a SECURITY.md file yet. With the availability of high-performance CPUs and GPUs, it is pretty much possible to solve every regression, classification, clustering, and other related problems using machine learning and deep learning models. However, there are still various portions that cause performance bottlenecks while developing such models. A large number of features in the dataset are one of the major factors that affect both the training time as well as the accuracy of machine learning models.

The Curse of Dimensionality is termed by mathematician R. Bellman in his book “Dynamic Programming” in 1957. According to him, the curse of dimensionality is the problem caused by the exponential increase in volume associated with adding extra dimensions to Euclidean space. In machine learning, “dimensionality” simply refers to the number of features (i.e. input variables) in your dataset. While the performance of any machine learning model increases if we add additional features/dimensions, at some point a further insertion leads to performance degradation that is when the number of features is very large...

This is called the “Curse of Dimensionality”. The curse of dimensionality basically means that the error increases with the increase in the number of features. It refers to the fact that algorithms are harder to design in high dimensions and often have a running time exponential in the dimensions. We need a better way to deal with such a high dimensional data so that we can quickly extract patterns and insights from it. So how do we approach such a dataset? Using dimensionality reduction techniques, indeed.

We can use this concept to reduce the number of features in our dataset without having to lose much information and keep (or improve) the model’s performance. Dimensionality reduction is a set of techniques that studies how to shrivel the size of data while preserving the most important information and further eliminating the curse of dimensionality. It plays an important role in the performance of classification and clustering problems. Libraries: NumPy pandas tensorflow matplotlib sklearn seaborn High correlation between two variables means they have similar trends and are likely to carry similar information. This can bring down the performance of some models drastically.

As a general guideline, we should keep those variables which show a decent or high correlation with the target variable. There was an error while loading. Please reload this page. This project has not set up a SECURITY.md file yet. This class goes deeper in the mechanism and usage of Dimensionality Reduction. You will learn to exploit the effects of dimensionality on your machine learning models, with illustrated intuitions, and practical examples.

You will start with an already very known technique in a linear setup, Principal Components Analysis, to move progressively towards non-linear manifold learning with t-SNE, to end up with Deep Learning techniques to reduce... Through concrete application of Autoencoders you will know what are their main interests and pitfalls, to make the best architecture choice given your problem to solve. This section provides implementation for concepts related to dimensionality reduction methods. The code in this section demonstrates the application of the dimensionality reduction methods to interesting datasets that illustrate the performance and nature of different dimensionality reduction methods. It also demonstrates the sequential sampling algorithms that use dimensionality reduction methods on the 10 dimensional Styblinski-Tang function. This illustrates the difficulty of performing sequential sampling on high-dimensional functions and the use of dimensionality reduction to improve the performance of sequential sampling.

The above concepts are covered in the following two sections: Sequential Sampling using Dimensionality Reduction There was an error while loading. Please reload this page.

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