10 روز تا تسلط بر مهندسی پرامپت، هوش مصنوعی مولد و علم داده

Introduction to 5 Projects in Prompt Engineering, Generative AI and Data Science

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About me, Diogo

Setting Up Google Colab

Setting Up Jupyter Notebook

Installing R and RStudio

Game Plan for Basics of Prompt Engineering

Understanding Transformers

Attention Mechanisms in NLP

Prompt Engineering Techniques

Setting Up the LM Studio

LM Studio - Explicit Instructions and One-Shot

LM Studio - Few-Shot

LLMs are Few-Shot Learners

LM Studio - Chain of Thoughts

Scientific Research on Chain of Thoughts

Key Learnings and Outcomes Prompt Engineering Basics

Game Plan for Prompt Engineering with System Message and LLM Parameters

Tokenization

OpenAI Tokenizer

Rock-Paper-Scissors, Dices and Strawberries

System Message

LM Studio - System Message

LM Studio - Breaking the System Message Part 1

LM Studio - Breaking the System Message Part 2

Understanding Generation Model Parameters

LM Studio - Parameters

Key Learnings and Outcomes System Message and LLM Parameters

Game Plan for Reasoning and Hallucinations

Meta Prompting

Analogical Reasoning Prompting

Rephrase and Respond

According-to Prompting

Multi-Persona Collaboration

Emotion Prompting

Key Learnings and Outcomes Reasoning and Hallucinations for LLMs

Game Plan LLM Reasoning Models

How LLM Reasoning Models work

Prompting Reasoning Models (OpenAI Focus)

Overthinking - LLM Reasoning Prompt Injection

Overthinking in Action

LLMs can't Reason - Apple Research Paper

Game Plan for OpenAI API

OpenAI API for Text

Python - Setting Up OpenAI API Key

Python - OpenAI API Setup

Python - Generating Text with OpenAI API

Python - OpenAI API Parameters

Python - OpenAI API with Few-Shot

Key Learning and Outcomes OpenAI API for Text

Project Introduction Can GPT play rock paper scissors

Python - OpenAI API Setup

Python - Random Playing Strategy

Python - Iteratively Improving the System Prompt

Python - Testing Temperature Parameters

Python - New Strategy Change if Defeat

Python - Building Functions to Play Game

Python - New Strategy The Analyst

Python - Testing Strategies

Game Plan for OpenAI API for Images

Python - OpenAI API Setup

Python - Analyzing images from Links

Python - Encoding Images

Python - Analyzing Images from base64

Python - Adding Google Searches

Game Plan for Random Forest

Random Forest and Ensemble Learning

How Decision Trees Work

Python - Setup

Python - Data Processing

Python - Correlation Heatmap

Python - Training and Test Set

Python - Random Forest

Evaluation metrics for Classication Problems Part 1

Evaluation metrics for Classication Problems Part 2

Python - Assessing Classification Model

Python - Parameter Tuning

Python - Parameter Tuning 2

Python - Best Random Forest Model

Python - Feature Importance

Python - Charting a Great Plot Part 1

Python - Charting a Great Plot Part 2

Python - Charting a Great Plot Part 3

Key Learnings and Outcomes Random Forest

Problem Statement

Introducing XGBoost

How XGBoost works

XGBoost quirks

Dummy variable trap

Root Square Mean Error

Variance vs. Bias trade off

Parameter tuning and Cross Validation

SHAP Values

Python - XGBoost Setup

Python - Data Processing

Python - First XGBoost Model

Python - Evaluating XGBoost Model

Python - Evaluating XGBoost Model part 2

Python - Building Functions for Binary Model Assessment

Python - Data Processing Part 2

Python - Second XGBoost Model

Random Parameter Tuning

Python - Parameter Tuning

Python - Final XGBoost Model

Python - SHAP Importance and Summary Plot

Python - SHAP Dependence and Force Plots

Python - SHAP Waterfall and Cohort Deep Dives

R - Loading and inspecting data

Isolating numerical variables

R - Summary Statistics and Correlation Matrix

R - Preparing first dataset

R - Training and test set

R - Isolating X and Y variables

R - Setting XGBoost Parameters

R - Parallel Processing

R - Running XGBoost

R - Predicting with XGBoost

R - Confusion Matrix

R - Transforming factors into numerical variables

R - Preparing final dataset

R - Second XGBoost model

R - Predictions and Confusion Matrix part 2

R - Start Parallel Processing

R - Cross Validation inputs

R - Cross Validation Parameters

R - Parameters to tune

R - Parameter Tuning round 1

R - Parameter Tuning round 2

R - Final XGBoost model

R - Business Perspective

R - Importance Drivers and SHAP Values

Game Plan for CrewAI

CrewAI

Python - CrewAI Setup

Python - First AI Agent

Python - Task and AI Crew

Python - Second AI Agent and Task

Python - Third AI Agent