programming notes blog

  **Reinforcement Learning Algorithms:** 1.  **Reinforcement Learning from Human Feedback (RLHF):**    - **Simple Explanation:** RLHF is a method where we improve a model by using feedback from humans.  The model learns to give better answers based on what people prefer.    - **Why Use It with Llama 3.1:** We can make Llama 3.1 respond more like a human by teaching it what answers people like, making it more helpful. 2.  **Proximal Policy Optimization (PPO):**    - **Simple Explanation:** PPO is a technique that helps a model learn safely and efficiently.  It updates the model in small steps to avoid big mistakes.    - **Why Use It with Llama 3.1:** By using PPO, we can train Llama 3.1 without risking large errors, leading to steady improvements. 3.  **Direct Preference Optimization (DPO):**    - **Simple Explanation:** DPO lets the model learn directly from what people prefer, without needing extra steps....

Certainly! Here's a tutorial in the form of a flash card table for MySQL SQL commands: SQL Command & Explanation SQL Example SELECT - Retrieves data from one or more tables. SELECT first_name, last_name FROM employees; FROM - Specifies the table to retrieve data from. SELECT first_name FROM employees; WHERE - Filters records based on a condition. SELECT * FROM employees WHERE age > 30; AND , OR - Combine conditions in a WHERE clause. SELECT * FROM employees WHERE age > 30 AND department = 'HR'; ORDER BY - Sorts the result set. SELECT * FROM employees ORDER BY last_name ASC; INSERT INTO - Adds new records into a table. INSERT INTO employees (first_name, last_name) VALUES ('John', 'Doe'); UPDATE - Modifies existing records in a table. UPDATE employees SET age = 31 WHERE first_name = 'John' AND last_name = 'Doe'; DELETE - Removes records from a table. DELETE FROM employees WHERE last_name = 'Doe'; CREATE DATABASE - Cre...