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Reasoning Capabilities in LLMs

Large Language Models (LLMs) have evolved significantly in their reasoning capabilities, enabling them to tackle complex tasks that require logical deduction, problem-solving, and contextual understanding. Below, I’ll explain the reasoning capabilities of LLMs, provide examples, highlight specific models, and offer a comparison. Reasoning Capabilities in LLMs LLMs exhibit reasoning through their ability to process and […]

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NLP – Understanding TF-IDF

TF-IDF, or Term Frequency-Inverse Document Frequency, is a crucial measure in NLP and Information Retrieval that assesses a word’s significance in a document relative to a broader corpus. It combines term frequency and inverse document frequency to highlight meaningful terms, aiding in search engines, document clustering, and spam detection, while having some limitations.

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NLP – Understanding Bag of Words (BoW)

Bag of Words (BoW) is a key Natural Language Processing (NLP) technique that transforms text into numerical formats by counting word frequencies, disregarding grammar and context. Though simple, it is popular for numerous applications, including text classification and information retrieval. Its limitations include lack of context awareness and inability to capture semantics.

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Understanding Stemming and Lemmatization in NLP

Stemming and Lemmatization are text normalization techniques in Natural Language Processing (NLP) that reduce words to their base forms, but they differ in their approach: stemming is a rule-based, fast, and potentially inaccurate method, while lemmatization is context-aware, dictionary-based, and more accurate but slower. Stemming Definition: A heuristic process that removes suffixes or prefixes from

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What are the different dimensionality reduction techniques?

Dimensional reduction is a technique used in data processing to reduce the number of input variables in a dataset. This process simplifies the data while retaining its essential characteristics, which can help in several ways, such as improving model performance, reducing computational cost, and facilitating data visualization. Here are some common methods and concepts related

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Candlestick Mastery – Introduction

Candlestick mastery is understanding and using candlestick patterns to make informed trading decisions. Candlestick patterns are graphical representations of price movement, and they can be used to identify trends, reversals, and support and resistance levels. There are many different candlestick patterns, but some of the most common and important ones include: Bullish patterns: Bullish patterns suggest

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Essentials of Data Science – Probability and Statistical Inference – Normal Approximation

In this note series on Probability and Statistical Inference, we have already seen the importance of probability distributions and their associated probability functions for discrete random variables and continuous random variables. In addition, we have learned to resemble a natural random phenomenon with these probability distributions. These distributions were Degenerate distribution, Uniform distribution, Bernoulli distribution, Binomial distribution, Poisson distribution, Geometric distribution, and Normal

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