· MachineLearning

Neural Networks Cheat Sheet

Network Type Core Idea Key Components Typical Use Cases Notes
Feedforward (MLP) One way flow input to output Dense layers, activations Tabular, regression Baseline model
CNN Spatial feature extraction Convolution, pooling Images, video Translation invariant
RNN Sequence modeling with state Hidden state Time series, text Vanishing gradients
LSTM Gated long memory Input forget output gates Long sequences Stable training
Transformer Attention based modeling Self attention, multi head NLP, vision Highly parallel
Autoencoder Compression via reconstruction Encoder, decoder Anomaly detection Unsupervised
GAN Adversarial generation Generator, discriminator Image synthesis Training instability
GNN Graph message passing Node edge embeddings Networks, molecules Relational reasoning
PINN Physics constrained learning PDE residuals Scientific computing Data efficient

Topics

  • Machine Learning
  • Neural Networks
  • Deep Learning