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Data Science In Python: Unsupervised Learning

Data Science In Python: Unsupervised Learning
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Alice-zhao , Pythondimensionality-reductionuse , Maven-analytics , Data-science , Machine-learning , K-means-clustering , Hierarchical-clustering , Isolation-forests , Principal-component-analysis , Cosine-similarity , Singular-value-decomposition

Proof of the Singular Value Decomposition

Proof of the Singular Value Decomposition
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Gilbert-strang , Singular-value-decomposition , Vd , Igendecomposition , Atrix-factorization , Rincipal-components-analysis , Ca , Ata-analysis , Tatistical-modeling , Achine-learning , Tatistics

Beyond Self-Attention: How a Small Language Model Predicts the Next Token

A deep dive into the internals of a small transformer model to learn how it turns self-attention calculations into accurate predictions for the next token.

Andrej-karpathy , Jeremy-kun , Network-outputs , Block-structure , Proposal-in-action , Transformer-output , Feed-forward-network-outputs , Procedure-setup , First-block , Why-does , Vector-addition , Transformer-block-structure

SVD Image Compression, Explained

Singular Value Decomposition (SVD) is a fundamental concept in linear algebra, and it is particularly important in the field of machine learning for tasks such as dimensionality reduction, data compression, and noise reduction.

Singular-value-decomposition ,

Diagnosing Mental Health Disorders Through AI Facial Expression Evaluation

Researchers from Germany have developed a method for identifying mental disorders based on facial expressions interpreted by computer vision. The new approach can not only distinguish between unaffected and affected subjects, but can also correctly distinguish depression from schizophrenia, as we

Germany , Logitech , University-hospital-at-aachen , Opto-electronic-encephalography , Affective-disorders , University-hospital , Singular-value-decomposition ,

Kangaroo Court: Analyzing Relationships Between Documents: A Brief Introduction to LSI/LSA | Association of Certified E-Discovery Specialists (ACEDS)

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Within the field of Natural Language Processing (NLP) there are a number of techniques that can be deployed for the purpose of information retrieval and understanding the relationships between documents. The growth in unstructured data requires better methods for legal teams to cut through and understand these relationships as efficiently as possible. The simplest way of finding similar documents is by using vector representation of text and cosine similarity. One method for concept searching and determining semantics between phrases is Latent Semantic Indexing/Latent Semantic Analysis (LSI/LSA).

Jerome-bellegarda , Susan-dumais , Microsoft-research , Mexico-state-university , Association-of-certified-specialists , Certifiede-discovery-specialists , Natural-language-processing , Latent-semantic , Latent-semantic-analysis , Singular-value-decomposition-technique , Bag-of-words-model , Singular-value-decomposition