Showing results for study Vector Vector Vector Vector Product
GitHub Repo
https://github.com/abellaouikenza/predictive-algorithms-for-image-classification-tasks-using-the-Fashion-MNIST-dataset
abellaouikenza/predictive-algorithms-for-image-classification-tasks-using-the-Fashion-MNIST-dataset
The study showcases three independent models—XGBoost, Support Vector Machine (SVM), and Convolutional Neural Network (CNN)—to examine their efficacy in automated pattern recognition. The major goal of the study is to identify the model with the greatest accuracy in categorizing fashion products.
GitHub Repo
https://github.com/arpit3043/Extractive-Text-Summerization
arpit3043/Extractive-Text-Summerization
Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.
GitHub Repo
https://github.com/segfaultscribe/SIMD-Dot-product-Optimization
segfaultscribe/SIMD-Dot-product-Optimization
Performance case study of dot product optimizations using SIMD (SSE, AVX, AVX2), analyzing speedups from scalar to vectorized implementations with benchmarking and profiling.
GitHub Repo
https://github.com/somjit101/NLP-CaseStudy-Amazon-Fine-Foods-Review
somjit101/NLP-CaseStudy-Amazon-Fine-Foods-Review
Efficient Sentencing Encoding and Vectorization techniques with customer reviews on a product page of the popular E-Commerce website, Amazon using proven NLP techniques for the purpose of sentiment analysis.
GitHub Repo
https://github.com/rmlaylo/The-Price-is-Right
rmlaylo/The-Price-is-Right
Analyzed and organized a large dataset from LAZADA collected via API and web scraping. This study improved product categorization accuracy from 46% to over 60% by switching from Count Vector to TF-IDF, significantly enhancing cosine similarity.
GitHub Repo
https://github.com/DataQUEEN99/NeuroFlow-AI-_Personal-AI-Brain-for-Learning-Productivity
DataQUEEN99/NeuroFlow-AI-_Personal-AI-Brain-for-Learning-Productivity
A next-generation full-stack AI Personal Operating System (AI-POS) that acts as your persistent digital brain. NeuroFlow AI remembers your goals, tracks learning, executes tasks autonomously, and continuously optimizes your work, study, and life. Built with Next.js, React, Tailwind CSS, FastAPI, and vector memory for intelligent long-term context.
GitHub Repo
https://github.com/MeghanHan/PA3-Comparing-Classifiers
MeghanHan/PA3-Comparing-Classifiers
This study compares the performance of the classifiers (k-nearest neighbors, logistic regression, decision trees, and support vector machines). The dataset is related to the marketing of bank products over the telephone.
GitHub Repo
https://github.com/MIJUMBO/Cosine_Similarity_Case_Study
MIJUMBO/Cosine_Similarity_Case_Study
Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Similarity measures have a multitude of uses in machine learning projects; they come in handy when matching strings, measuring distance, and extracting features.
GitHub Repo
https://github.com/byukan/Marketing-Data-Science
byukan/Marketing-Data-Science
Analytics and data science business case studies to identify opportunities and inform decisions about products and features. Topics include Markov chains, A/B testing, customer segmentation, and machine learning models (logistic regression, support vector machines, and quadratic discriminant analysis).
GitHub Repo
https://github.com/UtkuKacar/Time-Series-Analysis-of-Electricity-Production