{ "api": "YAML JSON TOON Database", "version": "1.0.0", "format": "json", "dataset": { "id": 2850, "slug": "ml-model-architectures", "title": "Machine Learning Model Architectures", "description": "Overview of major ML model families: feedforward neural networks, CNNs, RNNs\/LSTMs, Transformers, GANs, and when to use each.", "summary": "Machine-learning model architectures with type, structure, layers, uses, pros, cons and notes: feedforward networks, CNNs, RNNs, LSTMs, transformers, GANs and autoencoders, with a selection guide by data type.", "use_when": [ "You are choosing an architecture for a data type such as images, text or time series.", "You need the strengths and weaknesses of each." ], "caveats": [ "Guidance is general; strong results often come from architecture, data and training choices together.", "Architectures are combined in practice (convolutional layers feeding transformers, for example)." ], "related": [ "neural-network-types", "attention-mechanisms", "ml-training-algorithms", "transfer-learning" ], "entry_count": 7, "fields": [ { "section": "architectures", "field": "name", "type": "string", "example": "Feedforward Neural Network (FNN)" }, { "section": "architectures", "field": "type", "type": "string", "example": "Dense \/ MLP" }, { "section": "architectures", "field": "structure", "type": "string", "example": "Input \u2192 Hidden layers (dense) \u2192 Output. No cycl\u2026" }, { "section": "architectures", "field": "layers", "type": "array", "example": "[3 items]" }, { "section": "architectures", "field": "use_cases", "type": "array", "example": "[3 items]" }, { "section": "architectures", "field": "pros", "type": "array", "example": "[3 items]" }, { "section": "architectures", "field": "cons", "type": "array", "example": "[2 items]" }, { "section": "architectures", "field": "notes", "type": "string", "example": "Also called Multi-Layer Perceptron (MLP); found\u2026" }, { "section": "architectures", "field": "key_layers", "type": "object", "example": "{convolution,pooling,fully_connected}" }, { "section": "architectures", "field": "variants", "type": "array|object", "example": "[3 items]" }, { "section": "architectures", "field": "gates", "type": "object", "example": "{forget_gate,input_gate,output_gate}" }, { "section": "architectures", "field": "core_mechanism", "type": "string", "example": "Self-attention: each token attends to all other\u2026" }, { "section": "architectures", "field": "training", "type": "string", "example": "Alternating: train D on real\/fake, then train G\u2026" }, { "section": "selection_guide", "field": "*", "type": "object", "example": "{tabular_data,images,sequences_short,sequences_long}" } ], "category": "AI & ML Terms", "category_slug": "ai-ml-terms", "tags": "machine-learning,neural-networks,architectures,cnn,rnn,transformer,gan", "view_count": 44, "created_at": 1777673262, "updated_at": 1777673262 }, "data": { "architectures": [ { "name": "Feedforward Neural Network (FNN)", "type": "Dense \/ MLP", "structure": "Input \u2192 Hidden layers (dense) \u2192 Output. No cycles or feedback connections.", "layers": [ "Input layer", "One or more hidden layers (ReLU\/common)", "Output layer (linear\/softmax\/sigmoid)" ], "use_cases": [ "Tabular data classification\/regression", "Simple pattern recognition", "Baseline model" ], "pros": [ "Simple to implement", "Universal approximator (with enough width\/depth)", "Fast inference" ], "cons": [ "Ignores spatial\/temporal structure", "Parameter count grows quickly with input size" ], "notes": "Also called Multi-Layer Perceptron (MLP); foundation for all deep nets" }, { "name": "Convolutional Neural Network (CNN)", "type": "Spatial hierarchy", "structure": "Convolutional layers (filters) \u2192 pooling \u2192 fully connected. Local receptive fields, weight sharing.", "key_layers": { "convolution": "Applies filters\/kernels to detect local patterns (edges, textures)", "pooling": "Downsampling (max\/avg) for translation invariance", "fully_connected": "Final classification\/regression layers" }, "use_cases": [ "Image classification", "Object detection", "Medical imaging", "Time series (1D conv)" ], "pros": [ "Parameter sharing (efficient)", "Spatial invariance", "Detects hierarchical features" ], "cons": [ "Fixed receptive field (unless dilated)", "Not inherently good for sequential data" ], "notes": "Architecture depth (ResNet, Inception) matters more than width for vision" }, { "name": "Recurrent Neural Network (RNN)", "type": "Sequential \/ temporal", "structure": "Recurrent connections; hidden state carries information across time steps.", "variants": [ "Vanilla RNN (simple recurrence)", "LSTM (Long Short-Term Memory)", "GRU (Gated Recurrent Unit)" ], "use_cases": [ "Language modeling", "Time series prediction", "Speech recognition", "Sequence tagging" ], "pros": [ "Handles variable-length sequences", "Theoretically infinite memory (with LSTM\/GRU)" ], "cons": [ "Sequential processing (hard to parallelize)", "Vanishing\/exploding gradients (vanilla RNN)", "LSTM\/GRU complex" ], "notes": "LSTM\/GRU gates mitigate vanishing gradients; still largely superseded by Transformers for NLP" }, { "name": "Long Short-Term Memory (LSTM)", "type": "Recurrent with gates", "structure": "Cell state + forget\/input\/output gates. Controls information flow over long sequences.", "gates": { "forget_gate": "Decide what to discard from cell state", "input_gate": "Decide what new information to store", "output_gate": "Decide what to output based on cell state" }, "use_cases": [ "Machine translation (legacy)", "Speech synthesis", "Text generation (pre-Transformer)" ], "pros": [ "Can learn long-range dependencies (~100s steps)", "Robust to vanishing gradients" ], "cons": [ "Still sequential (slow training)", "Many parameters (4\u00d7 vanilla RNN)", "Requires more data than simpler models" ], "notes": "LSTM superior to vanilla RNN but slower; Transformer now standard for sequence" }, { "name": "Transformer", "type": "Attention-based", "structure": "Encoder-decoder or encoder-only\/decoder-only. Self-attention + feed-forward. Positional encoding.", "core_mechanism": "Self-attention: each token attends to all others; O(n\u00b2) complexity", "variants": { "encoder_only": "BERT (masked attention, pre-training)", "decoder_only": "GPT (causal attention, autoregressive)", "encoder-decoder": "T5, original Transformer (translation)" }, "use_cases": [ "NLP (all major tasks)", "Computer vision (Vision Transformer)", "Audio (Whisper)", "Multimodal (CLIP)" ], "pros": [ "Parallelizable (no recurrence)", "Long-range dependencies (attention)", "Scalable with data\/model size" ], "cons": [ "Quadratic memory in sequence length", "Requires massive data for training from scratch", "Positional encoding limits context" ], "notes": "Dominant architecture since 2017 (Attention Is All You Need). Sparse attention variants handle long sequences." }, { "name": "Generative Adversarial Network (GAN)", "type": "Generative", "structure": "Generator (creates fake samples) + Discriminator (distinguishes real vs fake). Min-max game.", "training": "Alternating: train D on real\/fake, then train G to fool D. No explicit likelihood.", "use_cases": [ "Image synthesis (StyleGAN)", "Image-to-image translation", "Super-resolution", "Data augmentation" ], "pros": [ "High-quality, sharp samples", "No explicit likelihood modeling (flexible)" ], "cons": [ "Training unstable (mode collapse, vanishing gradients)", "Hard to evaluate", "No explicit density" ], "notes": "Diffusion models now dominate image generation; GANs still used for specific tasks (style transfer)" }, { "name": "Autoencoder", "type": "Representation learning", "structure": "Encoder (compress to latent) \u2192 bottleneck \u2192 Decoder (reconstruct). Reconstruction loss trains end-to-end.", "variants": [ "Vanilla AE (dense layers)", "Variational Autoencoder (VAE) \u2014 probabilistic latent", "Denoising AE \u2014 robust features" ], "use_cases": [ "Dimensionality reduction", "Anomaly detection", "Denoising", "Generative modeling (VAE)" ], "pros": [ "Unsupervised pre-training possible", "Learns compact representations" ], "cons": [ "VAE samples blurry", "Simple AE doesn't learn useful latent space", "Reconstruction loss not always semantic" ], "notes": "Contrastive learning (SimCLR) often superior for representation learning" } ], "selection_guide": { "tabular_data": "FNN or Gradient Boosting (XGBoost\/LightGBM)", "images": "CNN (ResNet, EfficientNet) or Vision Transformer", "sequences_short": "LSTM\/GRU or Transformer (if enough data)", "sequences_long": "Transformer with sparse attention or linear attention", "text_nlp": "Transformer (BERT for understanding, GPT for generation)", "time_series": "Temporal CNN, LSTM, or Transformer (Informer, PatchTST)", "image_generation": "Diffusion model (Stable Diffusion) or GAN (legacy)", "anomaly_detection": "Autoencoder or isolation forest" } } }