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Most people don’t know how Gen AI really works. I’ve explained core models in simple way below. 1. 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 They learn by 𝗮𝗱𝗱𝗶𝗻𝗴 𝗻𝗼𝗶𝘀𝗲 to data and then learning how to 𝗿𝗲𝗺𝗼𝘃𝗲 𝘁𝗵𝗮𝘁 𝗻𝗼𝗶𝘀𝗲 step by step. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 1. Start with a clean image. 2. Gradually add random noise until the image looks like static. 3. Train a model to reverse this process. 4. The model removes noise step by step. 5. A brand-new image appears. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹: • Very good at creating 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝗺𝗮𝗴𝗲𝘀 • Produces realistic details 𝗘.𝗴: AI image generation, Art creation. _____________ 2. 𝗚𝗔𝗡𝘀 (𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗱𝘃𝗲𝗿𝘀𝗮𝗿𝗶𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀) Two AI models 𝗰𝗼𝗺𝗽𝗲𝘁𝗲 𝘄𝗶𝘁𝗵 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿 to create realistic data. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 1. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿 creates fake data (like a fake face). 2. 𝗗𝗶𝘀𝗰𝗿𝗶𝗺𝗶𝗻𝗮𝘁𝗼𝗿 checks if the data is real or fake. 3. Discriminator gives feedback. 4. Generator improves. 5. This competition repeats many times. 6. Generator becomes very good at making realistic data. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹: • Can create very realistic images • Fast generation 𝗘.𝗴: Deepfake videos, Art generation. _____________ 3. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 They understand 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘄𝗼𝗿𝗱𝘀, 𝗰𝗼𝗱𝗲, 𝗼𝗿 𝗶𝗺𝗮𝗴𝗲 𝗽𝗮𝗿𝘁𝘀 using attention. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 1. Input is broken into small parts (tokens). 2. The model checks how each token relates to others. 3. Important parts get more attention. 4. The model predicts the next output based on context. 5. Output is generated smoothly. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹: • Understands long context • Works well with language and reasoning 𝗘.𝗴: Text/code generation, Image understanding. _____________ 4. 𝗔𝘂𝘁𝗼𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 They generate data 𝗼𝗻𝗲 𝘀𝘁𝗲𝗽 𝗮𝘁 𝗮 𝘁𝗶𝗺𝗲, using previous output. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 1. Model predicts the first element. 2. Uses that output to predict the next one. 3. Repeats this process again and again. 4. Output grows step by step. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹: • Very good for sequences • Keeps logical flow 𝗘.𝗴: Text generation, Time-series data. _____________ 5. 𝗩𝗮𝗿𝗶𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝘂𝘁𝗼𝗲𝗻𝗰𝗼𝗱𝗲𝗿𝘀 (𝗩𝗔𝗘𝘀) They 𝗰𝗼𝗺𝗽𝗿𝗲𝘀𝘀 𝗱𝗮𝘁𝗮, then recreate new variations from it. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 1. Input data is encoded into a compact form (latent space). 2. The model learns patterns inside this space. 3. It samples from this space. 4. Decodes it back into new data. 5. Output looks similar but not identical to the input. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹: • Good at learning structure • Produces smooth variations 𝗘.𝗴: Image reconstruction, Anomaly detection, Creative design. Understanding fundamental models matters more than just chasing GenAI tools. ✅ Repost so others can understand the fundamental of these models.