Lilly Tech Systems
AI Architecture
Master the fundamental neural network architectures that power every modern AI system — from Transformers and CNNs to Diffusion Models, GANs, State Space Models, and beyond. Understand how each architecture works, when to use it, and how they shape the AI landscape in 2025.
Your Learning Path
Follow these lessons in order for a complete understanding of AI architectures, or jump to any topic that interests you.
1. Introduction
The evolution of AI architectures from perceptrons to modern transformers. A comprehensive comparison of all 12 architectures, how to choose the right one, and the building blocks they share.
2. Transformer
The architecture that changed everything. Self-attention, multi-head attention, positional encoding, encoder-decoder stacks, GPT vs BERT vs T5, and scaling laws.
3. CNN
Convolutional Neural Networks explained: convolution operations, pooling, classic architectures from LeNet to EfficientNet, residual connections, and CNNs vs Vision Transformers.
4. RNN, LSTM & GRU
Recurrent architectures for sequential data. Vanilla RNNs, the vanishing gradient problem, LSTM gates, GRU simplifications, and bidirectional variants.
5. Encoder-Decoder
The universal pattern for sequence-to-sequence tasks. Encoder compression, decoder generation, cross-attention, and applications from translation to summarization.
6. Attention Mechanisms
Deep dive into attention: Bahdanau vs Luong, self-attention, cross-attention, multi-head attention, flash attention, sparse attention, and linear attention variants.
7. Diffusion Models
How DALL-E, Stable Diffusion, and Midjourney work. Forward and reverse diffusion, denoising score matching, U-Net backbone, classifier-free guidance, and latent diffusion.
8. GAN Architecture
Generative Adversarial Networks: generator vs discriminator, training dynamics, mode collapse, DCGAN, StyleGAN, CycleGAN, and conditional generation.
9. Mixture of Experts
How MoE scales models to trillions of parameters efficiently. Gating networks, sparse routing, expert specialization, load balancing, and models like Mixtral and GPT-4.
10. State Space Models
The emerging challenger to Transformers. S4, Mamba, structured state spaces, linear-time sequence modeling, and why SSMs matter for long contexts.
11. Graph Neural Networks
Learning on graph-structured data. Message passing, GCN, GAT, GraphSAGE, knowledge graphs, molecular modeling, and social network analysis.
12. Autoencoders & VAEs
Learning compressed representations. Vanilla autoencoders, denoising, sparse, variational autoencoders, the reparameterization trick, and latent space interpolation.
13. Emerging Architectures
What comes next: RWKV, RetNet, Hyena, xLSTM, neuromorphic computing, quantum neural networks, and the future direction of AI architecture research.
What You'll Learn
By the end of this course, you will be able to:
Understand Core Architectures
Know how Transformers, CNNs, RNNs, GANs, Diffusion Models, and other architectures work internally — from the mathematical foundations to practical implementations.
Choose the Right Architecture
Given any AI task — text, vision, generation, sequential data — confidently select the architecture best suited for the job based on data type, scale, and latency needs.
Read Research Papers
Navigate modern AI research with fluency. Understand architecture diagrams, attention formulas, training procedures, and ablation studies in any deep learning paper.
Design AI Systems
Architect production AI systems by combining multiple architectures — using CNNs for feature extraction, Transformers for reasoning, and Diffusion Models for generation.