Guys, this post is a must-read if you're even remotely curious about Generative AI & LLMs!
(Save it. Share it)
TOP 10 CONCEPTS YOU CAN'T IGNORE IN GENERATIVE AI
*1. Transformers โ The Magic Behind GPT*
Forget the robots. These are the real transformers behind ChatGPT, Bard, Claude, etc. They process all the text at once (not step-by-step like RNNs) making them super smart and insanely fast.
*2. Self-Attention โ The Eye of the Model*
This is how the model pays attention to every word while generating output. Like how you remember both the first and last scene of a movie โ self-attention lets AI weigh every wordโs importance.
*3. Tokenization โ Breaking It Down*
AI doesnโt read like us. It breaks sentences into tokens (words or subwords). Even โunbelievableโ gets split as โun + believ + ableโ โ thatโs why LLMs handle language so smartly.
*4. Pretraining vs Fine-tuning*
Pretraining = Learn everything from scratch (like reading the entire internet).
Fine-tuning = Special coaching (like teaching GPT how to write code, summarize news, or mimic Shakespeare).
*5. Prompt Engineering โ Talking to AI in Its Language*
A good prompt = better response. Itโs like giving AI the right context or setting the stage properly. One word can change everything. Literally.
*6. Zero-shot, One-shot, Few-shot Learning*
Zero-shot: Model does it with no examples.
One/Few-shot: Model sees 1-2 examples and gets the hang of it.
Think of it like showing your friend how to do a dance step once, and boomโthey nail it.
Here you can find more explanation on prompting techniques
๐๐
https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
*7. Diffusion Models โ The Art Geniuses*
Behind tools like MidJourney and DALLยทE. They work by turning noise into beautyโliterally. First they add noise, then learn to reverse it to generate images.
*8. Reinforcement Learning from Human Feedback (RLHF)*
AI gets better with feedback. This is the secret sauce behind making models like ChatGPT behave well (and not go rogue).
*9. Hallucinations โ AI's Confident Lies*
Yes, AI can make things up and sound 100% sure. Thatโs called a hallucination. Knowing when itโs real vs fake is key.
*10. Multimodal Models*
These are the models that donโt just understand text but also images, videos, and audio. Think GPT-4 Vision or Gemini. The future is not just text โ itโs everything together.
Generative AI is not just buzz. It's the backbone of a new era.
Credits: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
(Save it. Share it)
TOP 10 CONCEPTS YOU CAN'T IGNORE IN GENERATIVE AI
*1. Transformers โ The Magic Behind GPT*
Forget the robots. These are the real transformers behind ChatGPT, Bard, Claude, etc. They process all the text at once (not step-by-step like RNNs) making them super smart and insanely fast.
*2. Self-Attention โ The Eye of the Model*
This is how the model pays attention to every word while generating output. Like how you remember both the first and last scene of a movie โ self-attention lets AI weigh every wordโs importance.
*3. Tokenization โ Breaking It Down*
AI doesnโt read like us. It breaks sentences into tokens (words or subwords). Even โunbelievableโ gets split as โun + believ + ableโ โ thatโs why LLMs handle language so smartly.
*4. Pretraining vs Fine-tuning*
Pretraining = Learn everything from scratch (like reading the entire internet).
Fine-tuning = Special coaching (like teaching GPT how to write code, summarize news, or mimic Shakespeare).
*5. Prompt Engineering โ Talking to AI in Its Language*
A good prompt = better response. Itโs like giving AI the right context or setting the stage properly. One word can change everything. Literally.
*6. Zero-shot, One-shot, Few-shot Learning*
Zero-shot: Model does it with no examples.
One/Few-shot: Model sees 1-2 examples and gets the hang of it.
Think of it like showing your friend how to do a dance step once, and boomโthey nail it.
Here you can find more explanation on prompting techniques
๐๐
https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
*7. Diffusion Models โ The Art Geniuses*
Behind tools like MidJourney and DALLยทE. They work by turning noise into beautyโliterally. First they add noise, then learn to reverse it to generate images.
*8. Reinforcement Learning from Human Feedback (RLHF)*
AI gets better with feedback. This is the secret sauce behind making models like ChatGPT behave well (and not go rogue).
*9. Hallucinations โ AI's Confident Lies*
Yes, AI can make things up and sound 100% sure. Thatโs called a hallucination. Knowing when itโs real vs fake is key.
*10. Multimodal Models*
These are the models that donโt just understand text but also images, videos, and audio. Think GPT-4 Vision or Gemini. The future is not just text โ itโs everything together.
Generative AI is not just buzz. It's the backbone of a new era.
Credits: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
โค2๐2
Generative AI
Guys, this post is a must-read if you're even remotely curious about Generative AI & LLMs! (Save it. Share it) TOP 10 CONCEPTS YOU CAN'T IGNORE IN GENERATIVE AI *1. Transformers โ The Magic Behind GPT* Forget the robots. These are the real transformersโฆ
Guys, here are 10 more next-level Generative AI terms thatโll make you sound like youโve been working at OpenAI (even if you're just exploring)!
TOP 10 ADVANCED TERMS IN GENERATIVE AI (Vol. 2)
*1. LoRA (Low-Rank Adaptation)*
Tiny brain upgrades for big models. LoRA lets you fine-tune huge LLMs without burning your laptop. Itโs like customizing ChatGPT to think like you โ but in minutes.
*2. Embeddings*
This is how AI understands meaning. Every word or sentence becomes a string of numbers (vectors) in a high-dimensional space โ so "king" and "queen" end up close to each other.
*3. Context Window*
Itโs like the memory span of the model. GPT-3.5 has ~4K tokens. GPT-4 Turbo? 128K tokens. More tokens = model remembers more of your prompt, better answers, fewer โforgot what you saidโ moments.
*4. Retrieval-Augmented Generation (RAG)*
Want ChatGPT to know your documents or PDFs? RAG does that. It mixes search with generation. Perfect for building custom bots or AI assistants.
*5. Instruction Tuning*
Ever noticed how GPT-4 just knows how to follow instructions better? Thatโs because itโs been trained on instruction-style prompts โ "summarize this", "translate that", etc.
*6. Chain of Thought (CoT) Prompting*
Tell AI to think step by step โ and it will!
CoT prompting boosts reasoning and math skills. Just add โLetโs think step-by-stepโ and watch the magic.
*7. Fine-tuning vs. Prompt-tuning*
- Fine-tuning: Teach the model new behavior permanently.
- Prompt-tuning: Use clever inputs to guide responses without retraining.
You can think of it as permanent tattoo vs. temporary sticker. ๐
*8. Latent Space*
This is where creativity happens. Whether generating text, images, or music โ AI dreams in latent space before showing you the result.
*9. Diffusion vs GANs*
- Diffusion = controlled chaos (used by DALLยทE 3, MidJourney)
- GANs = two AIs fighting โ one generates, one critiques
Both create stunning visuals, but Diffusion is currently winning the art game.
*10. Agents / Auto-GPT / BabyAGI*
These are like AI with goals. They donโt just respond โ they act, search, loop, and try to accomplish tasks. Think of it like ChatGPT that books your flight and packs your bag.
React with โค๏ธ if it helps
If you understand even 5 of these terms, you're already ahead of 95% of the crowd.
Credits: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
TOP 10 ADVANCED TERMS IN GENERATIVE AI (Vol. 2)
*1. LoRA (Low-Rank Adaptation)*
Tiny brain upgrades for big models. LoRA lets you fine-tune huge LLMs without burning your laptop. Itโs like customizing ChatGPT to think like you โ but in minutes.
*2. Embeddings*
This is how AI understands meaning. Every word or sentence becomes a string of numbers (vectors) in a high-dimensional space โ so "king" and "queen" end up close to each other.
*3. Context Window*
Itโs like the memory span of the model. GPT-3.5 has ~4K tokens. GPT-4 Turbo? 128K tokens. More tokens = model remembers more of your prompt, better answers, fewer โforgot what you saidโ moments.
*4. Retrieval-Augmented Generation (RAG)*
Want ChatGPT to know your documents or PDFs? RAG does that. It mixes search with generation. Perfect for building custom bots or AI assistants.
*5. Instruction Tuning*
Ever noticed how GPT-4 just knows how to follow instructions better? Thatโs because itโs been trained on instruction-style prompts โ "summarize this", "translate that", etc.
*6. Chain of Thought (CoT) Prompting*
Tell AI to think step by step โ and it will!
CoT prompting boosts reasoning and math skills. Just add โLetโs think step-by-stepโ and watch the magic.
*7. Fine-tuning vs. Prompt-tuning*
- Fine-tuning: Teach the model new behavior permanently.
- Prompt-tuning: Use clever inputs to guide responses without retraining.
You can think of it as permanent tattoo vs. temporary sticker. ๐
*8. Latent Space*
This is where creativity happens. Whether generating text, images, or music โ AI dreams in latent space before showing you the result.
*9. Diffusion vs GANs*
- Diffusion = controlled chaos (used by DALLยทE 3, MidJourney)
- GANs = two AIs fighting โ one generates, one critiques
Both create stunning visuals, but Diffusion is currently winning the art game.
*10. Agents / Auto-GPT / BabyAGI*
These are like AI with goals. They donโt just respond โ they act, search, loop, and try to accomplish tasks. Think of it like ChatGPT that books your flight and packs your bag.
React with โค๏ธ if it helps
If you understand even 5 of these terms, you're already ahead of 95% of the crowd.
Credits: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
โค6๐2
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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