[ Сураба 🦀]

What is CRUD? CRUD stands for Create, Read, Update, and Delete. It represents the basic operations that can be performed on data in a database. Examples in SQL: 1. Create: Adding new records to a table.

    INSERT INTO students (id, name, age)
    VALUES (1, 'John Doe', 20);

2. Read: Retrieving data from a table.

    SELECT * FROM students;

3. Update: Modifying existing records.

    UPDATE students
    SET age = 21
    WHERE id = 1;

4. Delete: Removing records.

DELETE FROM students
WHERE id = 1;

🤓 TLDR: OpenAi and Elon saga (2014-2025) - Elon wanted to stop Demis & Deepmind from creating an AGI dictatorship. - He appointed himself as a CEO. - Greg and Ilya said: but Elon, now can easily become the AGI dictator. - Sam had his own party going, trying to get control, Ilya accused him being driven by money and politics instead of AGI. - Karpathy offered to merge OpenAi into Tesla. Elon liked the idea, others didn’t. - Elon said, let’s merge it with Tesla so that Tesla funds openai and keeps it being non profit. - Sam and Greg decided to find alternative ways to fund the company. - Sam wanted to do an IC0. Elon said it’ll make people think OpenAi is a scam. - They went for Microsoft deal instead. - Elon didn’t like the Microsoft deal and wanted to be the CEO, so he left in 2018 and stopped funding them. - Sam said he is enthusiastic about non profit future and earned CEO title. - Sam turned OpenAI into capped profit - Sam was fired by Ilya for thinking more about monetization than democratization of safe AGi. - Sam was brought back by Satya and Twitter “openai is nothing without its people” - Ilya was fired by Sam - Sam turned openai into for-profit. - Elon started XAI to build his own AgI - Ilya started SSI to build safe AGI - Greg left OpenAI, possibly tried to get into SSI, was rejected and returned back to Sam - But it all doesn’t really matter anymore, since LLMs reached the ceiling of scaling and won’t lead us to the AGI - Sam recently said “I know the worth to AGi now”, which is a fundraising play

A-Z of essential data science concepts A: Algorithm - A set of rules or instructions for solving a problem or completing a task. B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently. C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics. D: Data Mining - The process of discovering patterns and extracting useful information from large datasets. E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance. F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance. G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively. H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data. I: Imputation - The process of replacing missing values in a dataset with estimated values. J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously. K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups. L: Logistic Regression - A statistical model used for binary classification tasks. M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time. N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks. O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points. P: Precision and Recall - Evaluation metrics used to assess the performance of classification models. Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data. R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables. S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks. T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations. U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes. V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets. W: Weka - A popular open-source software tool used for data mining and machine learning tasks. X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks. Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters. Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

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