Post by @high_tech02

This infographic outlines the **Top 6 Types of AI Models**, providing their **descriptions, examples, and workflows**. Here's a breakdown: --- ### **1. Machine Learning Models** * **Description:** Learn from labeled/unlabeled data to detect patterns or make predictions. Includes: * Supervised * Unsupervised * Semi-supervised learning * **Examples:** Decision Trees, Random Forest, SVM, K-Means, XGBoost, PCA * **Workflow:** 1. Collect data 2. Clean & preprocess 3. Choose ML algorithm 4. Train model 5. Validate 6. Tune 7. Predict 8. Monitor & update --- ### **2. Deep Learning Models** * **Description:** Use deep neural networks to learn complex patterns from unstructured data (images, text, audio). * **Examples:** CNNs, RNNs, LSTMs, Transformers, GANs * **Workflow:** 1. Collect big data 2. Normalize 3. Build neural net 4. Forward pass 5. Compute error 6. Backpropagate 7. Update weights 8. Repeat 9. Output --- ### **3. Generative Models** * **Description:** Learn data distributions to generate new content (text, images, audio, code). * **Examples:** GPT-4, DALL·E, StyleGAN, MusicLM, AlphaCode * **Workflow:** 1. Train on dataset 2. Learn patterns 3. Receive input 4. Process through model 5. Sample output 6. Generate new content 7. Refine with feedback 8. Output --- ### **4. Hybrid Models** * **Description:** Combine various AI techniques (rule-based + neural) for robust solutions. * **Examples:** RAG (LLM + Search), AutoGPT, ensemble models * **Workflow:** 1. Combine models 2. Train individually 3. Build logic bridge 4. Input through pipeline 5. Route with logic 6. Aggregate outputs 7. Resolve conflicts 8. Final result --- ### **5. NLP Models (Natural Language Processing)** * **Description:** Process and understand human language for tasks like chatbots, translation, and summarization. * **Examples:** BERT, GPT-4, T5, Claude, RoBERTa * **Workflow:** 1. Clean text 2. Tokenize 3. Encode 4. Apply layers 5. Pass to model 6. Decode/classify 7. Post-process 8. Generate text --- ### **6. Computer Vision Models** * **Description:** Interpret visual data to detect features, patterns, or objects in images/videos. * **Examples:** YOLO, ResNet, VGGNet, EfficientNet, Mask R-CNN * **Workflow:** 1. Load image 2. Resize & normalize 3. Extract features 4. Apply CNN 5. Detect patterns 6. Classify 7. Post-process 8. Output --- This cheat sheet is a great visual summary of how AI model types differ in **function, application, and processing pipeline**. Let me know if you want a deeper dive into any specific model type!