Model Architecture Explorer

Understand the building blocks of AI systems

Transformer

2017

Language Models

Revolutionary architecture that uses self-attention mechanisms to process sequences in parallel....

CNN (Convolutional Neural Network)

1998

Vision Models

Specialized for processing grid-like data such as images using convolutional layers....

RNN (Recurrent Neural Network)

1986

Language Models

Processes sequential data by maintaining a hidden state across time steps....

LSTM (Long Short-Term Memory)

1997

Language Models

Advanced RNN architecture with gates to control information flow and memory....

GAN (Generative Adversarial Network)

2014

Vision Models

Two neural networks competing against each other to generate realistic data....

BERT (Bidirectional Encoder Representations from Transformers)

2018

Language Models

Transformer-based model pre-trained on masked language modeling for understanding tasks....

GPT (Generative Pre-trained Transformer)

2018

Language Models

Autoregressive transformer model trained to predict the next token in a sequence....

Vision Transformer (ViT)

2020

Vision Models

Applies transformer architecture directly to image patches for vision tasks....

Diffusion Models

2020

Vision Models

Generates data by gradually denoising random noise through learned reverse diffusion....

Transformer

Language ModelsIntroduced: 2017

Overview

Revolutionary architecture that uses self-attention mechanisms to process sequences in parallel.

Key Features

  • Self-attention mechanism
  • Positional encoding
  • Multi-head attention
  • Feed-forward networks
  • Layer normalization

Model Sizes

Varies: BERT (110M-340M), GPT-3 (175B), GPT-4 (1.7T estimated)

Common Use Cases

Translation Text generation Question answering Summarization

✅ Advantages

  • Parallel processing of sequences
  • Captures long-range dependencies
  • Highly scalable
  • State-of-the-art performance

⚠️ Limitations

  • High computational requirements
  • Large memory footprint
  • Quadratic complexity with sequence length

Learning Path

1

Start with Basics

Understand perceptrons and basic neural networks

2

Learn Core Concepts

Study backpropagation, activation functions, and optimization

3

Explore Architectures

Deep dive into specific architectures based on your interests

4

Hands-on Practice

Implement simple versions to truly understand the concepts