David Baum - Generative AI and LLMs for Dummies (2024).pdf
1.9 MB
Generative AI and LLMs for Dummies
David Baum, 2024
David Baum, 2024
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Here are 8 concise tips to help you ace a technical AI engineering interview:
๐ญ. ๐๐ ๐ฝ๐น๐ฎ๐ถ๐ป ๐๐๐ ๐ณ๐๐ป๐ฑ๐ฎ๐บ๐ฒ๐ป๐๐ฎ๐น๐ - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.
๐ฎ. ๐๐ถ๐๐ฐ๐๐๐ ๐ฝ๐ฟ๐ผ๐บ๐ฝ๐ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.
๐ฏ. ๐ฆ๐ต๐ฎ๐ฟ๐ฒ ๐๐๐ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ฒ๐ ๐ฎ๐บ๐ฝ๐น๐ฒ๐ - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.
๐ฐ. ๐ฆ๐๐ฎ๐ ๐๐ฝ๐ฑ๐ฎ๐๐ฒ๐ฑ ๐ผ๐ป ๐ฟ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.
๐ฑ. ๐๐ถ๐๐ฒ ๐ถ๐ป๐๐ผ ๐บ๐ผ๐ฑ๐ฒ๐น ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ๐ - Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc.
๐ฒ. ๐๐ถ๐๐ฐ๐๐๐ ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ถ๐ป๐ด ๐๐ฒ๐ฐ๐ต๐ป๐ถ๐พ๐๐ฒ๐ - Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks.
๐ณ. ๐๐ฒ๐บ๐ผ๐ป๐๐๐ฟ๐ฎ๐๐ฒ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฒ๐ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.
๐ด. ๐๐๐ธ ๐๐ต๐ผ๐๐ด๐ต๐๐ณ๐๐น ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐ - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
๐ญ. ๐๐ ๐ฝ๐น๐ฎ๐ถ๐ป ๐๐๐ ๐ณ๐๐ป๐ฑ๐ฎ๐บ๐ฒ๐ป๐๐ฎ๐น๐ - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.
๐ฎ. ๐๐ถ๐๐ฐ๐๐๐ ๐ฝ๐ฟ๐ผ๐บ๐ฝ๐ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.
๐ฏ. ๐ฆ๐ต๐ฎ๐ฟ๐ฒ ๐๐๐ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ฒ๐ ๐ฎ๐บ๐ฝ๐น๐ฒ๐ - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.
๐ฐ. ๐ฆ๐๐ฎ๐ ๐๐ฝ๐ฑ๐ฎ๐๐ฒ๐ฑ ๐ผ๐ป ๐ฟ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.
๐ฑ. ๐๐ถ๐๐ฒ ๐ถ๐ป๐๐ผ ๐บ๐ผ๐ฑ๐ฒ๐น ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ๐ - Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc.
๐ฒ. ๐๐ถ๐๐ฐ๐๐๐ ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ถ๐ป๐ด ๐๐ฒ๐ฐ๐ต๐ป๐ถ๐พ๐๐ฒ๐ - Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks.
๐ณ. ๐๐ฒ๐บ๐ผ๐ป๐๐๐ฟ๐ฎ๐๐ฒ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฒ๐ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.
๐ด. ๐๐๐ธ ๐๐ต๐ผ๐๐ด๐ต๐๐ณ๐๐น ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐ - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
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LLM Cheatsheet
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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Guys, Big Announcement!
Weโve officially crossed 4 Lakh followers on this journey together โ and itโs time to step up now! โค๏ธ
Iโm launching a Coding Interview Prep Series โ designed for everyone from beginners to those polishing their skills for FAANG-level interviews.
This will be a structured, step-by-step journey โ with short explanations, real coding examples, and mini-challenges after every topic to build real muscle memory.
Hereโs whatโs coming in the next few weeks:
Week 1: The Very Basics
- What is an Algorithm?
- What is Data Structure?
- Understanding Time Complexity (Big O Notation - made simple!)
- Basic Math for Coding Interviews
- Problem Solving Approach (How to break down a question)
Week 2: Arrays & Strings โ Your Building Blocks
- Introduction to Arrays and Strings
- Common Operations (Insert, Delete, Search)
- Two Pointer Techniques (Easy to Medium problems)
- Sliding Window Problems (Optimization techniques)
- String Manipulation Tricks for Interviews
Week 3: Hashing & Recursion
- HashMaps and HashSets (Power tools for coders!)
- Solving Problems using Hashing
- Introduction to Recursion
- Base Case and Recursive Case (Explained like a 5-year-old)
- Classic Recursion Problems
Week 4: Linked Lists, Stacks & Queues
- Singly vs Doubly Linked List
- Stack Operations and Problems (Valid Parentheses, Min Stack)
- Queue and Deque Concepts (with real examples)
- When to Use Stack vs Queue in Interviews
Week 5: Trees & Graphs Essentials
- Binary Trees and BST Basics
- Tree Traversals (Inorder, Preorder, Postorder)
- Graph Representations (Adjacency List, Matrix)
- Breadth-First Search (BFS) and Depth-First Search (DFS) explained simply
Week 6: Sorting, Searching & Interview Patterns
- Core Sorting Algorithms (Selection, Bubble, Insertion)
- Advanced Sorting (Merge Sort, Quick Sort)
- Binary Search Patterns (Find First, Last Occurrence, etc.)
- Mastering Interview Patterns (Two Sum, Three Sum, Subarray Sum, etc.)
Week 7: Dynamic Programming & Advanced Problem Solving
- What is Dynamic Programming (DP)?
- Top-Down vs Bottom-Up Approach
- Memoization and Tabulation Explained
- Classic DP Problems (Fibonacci, 0/1 Knapsack, Longest Subsequence)
Week 8: Real-World Mock Interviews
- Solving Medium to Hard Problems
- Tackling FAANG-level Interview Questions
- Tips to Handle Pressure in Coding Rounds
- Building the Right Mindset for Success
React with โค๏ธ if you're ready for this new coding series
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
Weโve officially crossed 4 Lakh followers on this journey together โ and itโs time to step up now! โค๏ธ
Iโm launching a Coding Interview Prep Series โ designed for everyone from beginners to those polishing their skills for FAANG-level interviews.
This will be a structured, step-by-step journey โ with short explanations, real coding examples, and mini-challenges after every topic to build real muscle memory.
Hereโs whatโs coming in the next few weeks:
Week 1: The Very Basics
- What is an Algorithm?
- What is Data Structure?
- Understanding Time Complexity (Big O Notation - made simple!)
- Basic Math for Coding Interviews
- Problem Solving Approach (How to break down a question)
Week 2: Arrays & Strings โ Your Building Blocks
- Introduction to Arrays and Strings
- Common Operations (Insert, Delete, Search)
- Two Pointer Techniques (Easy to Medium problems)
- Sliding Window Problems (Optimization techniques)
- String Manipulation Tricks for Interviews
Week 3: Hashing & Recursion
- HashMaps and HashSets (Power tools for coders!)
- Solving Problems using Hashing
- Introduction to Recursion
- Base Case and Recursive Case (Explained like a 5-year-old)
- Classic Recursion Problems
Week 4: Linked Lists, Stacks & Queues
- Singly vs Doubly Linked List
- Stack Operations and Problems (Valid Parentheses, Min Stack)
- Queue and Deque Concepts (with real examples)
- When to Use Stack vs Queue in Interviews
Week 5: Trees & Graphs Essentials
- Binary Trees and BST Basics
- Tree Traversals (Inorder, Preorder, Postorder)
- Graph Representations (Adjacency List, Matrix)
- Breadth-First Search (BFS) and Depth-First Search (DFS) explained simply
Week 6: Sorting, Searching & Interview Patterns
- Core Sorting Algorithms (Selection, Bubble, Insertion)
- Advanced Sorting (Merge Sort, Quick Sort)
- Binary Search Patterns (Find First, Last Occurrence, etc.)
- Mastering Interview Patterns (Two Sum, Three Sum, Subarray Sum, etc.)
Week 7: Dynamic Programming & Advanced Problem Solving
- What is Dynamic Programming (DP)?
- Top-Down vs Bottom-Up Approach
- Memoization and Tabulation Explained
- Classic DP Problems (Fibonacci, 0/1 Knapsack, Longest Subsequence)
Week 8: Real-World Mock Interviews
- Solving Medium to Hard Problems
- Tackling FAANG-level Interview Questions
- Tips to Handle Pressure in Coding Rounds
- Building the Right Mindset for Success
React with โค๏ธ if you're ready for this new coding series
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
โค8๐1
You can use ChatGPT to make money online.
Here are 10 prompts by ChatGPT
1. Develop Email Newsletters:
Make interesting email newsletters to keep audience updated and engaged.
Prompt: "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?"
2. Create Online Course Material:
Make detailed and educational online course content.
Prompt: "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?"
Read more......
Here are 10 prompts by ChatGPT
1. Develop Email Newsletters:
Make interesting email newsletters to keep audience updated and engaged.
Prompt: "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?"
2. Create Online Course Material:
Make detailed and educational online course content.
Prompt: "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?"
Read more......
๐1
List of AI Project Ideas ๐จ๐ปโ๐ป๐ค -
Beginner Projects
๐น Sentiment Analyzer
๐น Image Classifier
๐น Spam Detection System
๐น Face Detection
๐น Chatbot (Rule-based)
๐น Movie Recommendation System
๐น Handwritten Digit Recognition
๐น Speech-to-Text Converter
๐น AI-Powered Calculator
๐น AI Hangman Game
Intermediate Projects
๐ธ AI Virtual Assistant
๐ธ Fake News Detector
๐ธ Music Genre Classification
๐ธ AI Resume Screener
๐ธ Style Transfer App
๐ธ Real-Time Object Detection
๐ธ Chatbot with Memory
๐ธ Autocorrect Tool
๐ธ Face Recognition Attendance System
๐ธ AI Sudoku Solver
Advanced Projects
๐บ AI Stock Predictor
๐บ AI Writer (GPT-based)
๐บ AI-powered Resume Builder
๐บ Deepfake Generator
๐บ AI Lawyer Assistant
๐บ AI-Powered Medical Diagnosis
๐บ AI-based Game Bot
๐บ Custom Voice Cloning
๐บ Multi-modal AI App
๐บ AI Research Paper Summarizer
Join for more: https://t.iss.one/machinelearning_deeplearning
Beginner Projects
๐น Sentiment Analyzer
๐น Image Classifier
๐น Spam Detection System
๐น Face Detection
๐น Chatbot (Rule-based)
๐น Movie Recommendation System
๐น Handwritten Digit Recognition
๐น Speech-to-Text Converter
๐น AI-Powered Calculator
๐น AI Hangman Game
Intermediate Projects
๐ธ AI Virtual Assistant
๐ธ Fake News Detector
๐ธ Music Genre Classification
๐ธ AI Resume Screener
๐ธ Style Transfer App
๐ธ Real-Time Object Detection
๐ธ Chatbot with Memory
๐ธ Autocorrect Tool
๐ธ Face Recognition Attendance System
๐ธ AI Sudoku Solver
Advanced Projects
๐บ AI Stock Predictor
๐บ AI Writer (GPT-based)
๐บ AI-powered Resume Builder
๐บ Deepfake Generator
๐บ AI Lawyer Assistant
๐บ AI-Powered Medical Diagnosis
๐บ AI-based Game Bot
๐บ Custom Voice Cloning
๐บ Multi-modal AI App
๐บ AI Research Paper Summarizer
Join for more: https://t.iss.one/machinelearning_deeplearning
๐3