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Categories

Computer Science

Machine Learning

Activation Functions in Neural Networks

Bias-Variance Tradeoff

Computer Vision Fundamental Concepts

Cross-validation Techniques

Data Preprocessing for Machine Learning

Deep Learning Fundamentals

Data Imbalance Handling

Dimensionality Reduction Methods

Ensemble Learning Techniques

Evaluation Metrics for Classification

Feature Engineering Techniques

Generative Models

Gradient Descent Variants

Hyperparameter Tuning

Machine Learning Best Practices

Loss Functions Explained

Machine Learning Security and Privacy

Machine Learning Pipelines

Machine Learning Frameworks

Model Explainability and Interpretability

Multiclass Classification Strategies

Natural Language Processing Terminology

Model Selection Criteria

Neural Network Architectures

Performance Optimization in Machine Learning

Time Series Analysis Techniques

Reinforcement Learning Concepts

Statistical Tests for Model Comparison

Dimensionality Reduction Methods

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Linear Discriminant Analysis (LDA)

LDA attempts to model the difference between the classes of data. It finds the axes that maximize the separation between multiple classes.

Multidimensional Scaling (MDS)

MDS is a technique for analyzing similarity or dissimilarity data. It aims to place each object in N-dimensional space such that the between-object distances are preserved as well as possible.

Principal Component Analysis (PCA)

PCA works by calculating the eigenvectors and eigenvalues of the covariance matrix to find the principal components. Benefits include noise reduction and finding the underlying structure of the data.

Autoencoders

Autoencoders are neural networks designed to learn an encoding for the data. They work by compressing the input into a latent-space representation and then reconstructing the output from this representation.

Truncated Singular Value Decomposition (SVD)

Truncated SVD, also known as Latent Semantic Analysis in text processing, reduces dimensionality by transforming data to a lower-dimensional space, preserving only the most significant singular values.

t-Distributed Stochastic Neighbor Embedding (t-SNE)

t-SNE converts similarities between data points to joint probabilities and minimizes the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data.

Uniform Manifold Approximation and Projection (UMAP)

UMAP is a manifold learning technique that approximates a high-dimensional manifold. It works well even with large datasets, preserving more of the local and global data structure than t-SNE.

Isomap

Isomap extends PCA to non-linear dimension reduction by incorporating geodesic distances among all points on the manifold.

Independent Component Analysis (ICA)

ICA aims to represent a multivariate signal as a combination of independent non-Gaussian signals. It is commonly used for blind source separation.

Factor Analysis

Factor analysis is a model-based technique that attempts to describe variables as influenced by several latent factors. It reduces dimensionality by modeling the observed variables as linear combinations of potential factors plus noise.

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