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Understanding Model Types

Different AI tasks require different model architectures. Think of it like choosing the right tool for the job - you wouldn’t use a hammer to paint a wall.

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Language Models (LLMs)

The most versatile models that understand and generate human language.

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What They Do

Language models can:

• Answer questions
• Write content
• Translate languages
• Summarize text
• Generate code
• Follow instructions

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Common Models

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When to Use

Choose language models when you need:

• Natural language understanding
• Text generation
• Question answering
• Conversational AI
• Code generation

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Classification Models

Specialized for sorting things into categories.

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Text Classification

Categorize text into predefined groups:

• Sentiment analysis (positive/negative)
• Topic classification
• Intent detection
• Language detection

Best models: BERT, DistilBERT, RoBERTa

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Image Classification

Identify what’s in an image:

• Object recognition
• Medical diagnosis
• Quality control
• Content moderation

Best models: ResNet, EfficientNet, Vision Transformer (ViT)

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Multimodal Classification

Handle both text and images:

• Meme understanding
• Document analysis
• Product categorization

Best models: CLIP, LayoutLM, ALIGN

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Token Classification

Labels individual words or tokens in text.

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Named Entity Recognition (NER)

Find and label specific information:

• Names of people, places, organizations
• Dates and times
• Product names
• Medical terms

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Part-of-Speech Tagging

Identify grammatical roles:

• Nouns, verbs, adjectives
• Sentence structure analysis

Best models: BERT-NER, RoBERTa-token, SpaCy transformers

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Sequence-to-Sequence

Transform one sequence into another.

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Translation

Convert text between languages:

• Document translation
• Real-time chat translation
• Code translation

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Summarization

Condense long text:

• Article summaries
• Meeting notes
• Report digests

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Question Answering

Extract answers from context:

• Customer support
• Document Q&A
• Educational tools

Best models: T5, BART, mT5 (multilingual)

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Computer Vision Models

Process and understand images.

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Object Detection

Find and locate objects in images:

• Bounding boxes around objects
• Count items
• Track movement

Best models: YOLO, Faster R-CNN, DETR

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Image Segmentation

Pixel-level understanding:

• Medical imaging
• Autonomous driving
• Photo editing

Best models: U-Net, Mask R-CNN, SAM

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Image Generation

Create new images:

• Art generation
• Product visualization
• Data augmentation

Best models: Stable Diffusion, DALL-E, Midjourney

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Tabular Models

Work with structured data like spreadsheets.

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Regression

Predict continuous values:

• Price prediction
• Sales forecasting
• Risk scoring

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Classification

Categorize rows:

• Customer churn
• Fraud detection
• Disease diagnosis

Best models: XGBoost, CatBoost, TabNet

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Choosing the Right Model

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Consider Your Data

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Consider Your Resources

Limited Resources (< 8GB GPU)

• DistilBERT (66M parameters)
• MobileBERT (25M parameters)
• TinyBERT (15M parameters)

Moderate Resources (8-16GB GPU)

• BERT-base (110M parameters)
• GPT-2 small (124M parameters)
• RoBERTa-base (125M parameters)

Good Resources (24GB+ GPU)

• GPT-2 large (774M parameters)
• T5-large (770M parameters)
• LLaMA 7B (7B parameters)

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Consider Your Accuracy Needs

Speed over accuracy

• Use distilled models (DistilBERT, DistilGPT-2)
• Smaller architectures
• Quantized models

Accuracy over speed

• Use larger models
• Ensemble multiple models
• Longer training times

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Model Sizes and Trade-offs

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Parameters Count

Parameters are the adjustable parts of a model. More parameters usually mean:

• Better understanding
• Higher accuracy
• More memory needed
• Slower inference

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Size Guidelines

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Pre-trained vs From Scratch

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Use Pre-trained Models

99% of the time, start with a pre-trained model:

• Already understands language/images
• Needs less training data
• Faster to train
• Better results

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Train From Scratch Only When

• Working with unique data types
• Special domain (medical, legal)
• Custom architectures
• Research purposes

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Fine-tuning Strategies

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Full Fine-tuning

Update all model parameters:

• Best accuracy
• Needs more memory
• Risk of overfitting

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LoRA (Low-Rank Adaptation)

Update only small adapters:

• 90% less memory
• Faster training
• Slightly lower accuracy
• Perfect for large models

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Prompt Tuning

Train only prompt embeddings:

• Minimal memory
• Very fast
• Good for few-shot learning

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Freeze Strategies

Freeze some layers:

• Freeze early layers: Keep general features
• Freeze late layers: Keep task-specific features
• Gradual unfreezing: Start frozen, slowly unfreeze

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Multi-task Models

Some models can handle multiple tasks:

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T5 Family

• Text summarization
• Translation
• Question answering
• Classification

Just change the prompt prefix:

• “summarize: …”
• “translate English to French: …”
• “question: … context: …”

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FLAN Models

Pre-trained on many tasks:

• Better zero-shot performance
• More flexible
• Good instruction following

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Specialized Architectures

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Transformers

The current standard:

• Parallel processing
• Long-range dependencies
• Most modern models

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CNNs (Convolutional Neural Networks)

Still great for images:

• Efficient
• Well-understood
• Good for edge devices

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RNNs (Recurrent Neural Networks)

Older but still useful:

• Sequential data
• Time series
• Streaming applications

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Listen: Beyond LLMs - A Deep Dive

A 45-minute conversation about model types beyond language models, covering vision, tabular, and specialized architectures.

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Next Steps

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