DeepLearning Architectures

Before Transformers reshaped the world, three giants built the foundation.

It’s a focused architectural masterclass, crafted to decode the three pillars that powered the first deep learning era:

Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs).

In just 90-120 minutes, you’ll go from core intuition -> essential mathematics -> clean, readable code snippets.

You’ll leave with a deep mental model of each architecture’s strengths, limits, and legacy and see how they still shape today’s SOTA models.

Your 90-120 Minute Expedition:

Module I: The Architect’s Blueprint

• What is an "architecture"? Layers, blocks, and training loops.
• Core principles: Bias-variance, capacity, vanishing/exploding gradients.

Module II: The Universal Approximator, MLPs

• Activation functions: ReLU, sigmoid, tanh.
• Initialization (Xavier, He) and normalization (BatchNorm).
• Snippet: Build a 3-layer MLP from scratch (forward pass).

Module III: The Vision Masters — CNNs

• Convolutions, padding, stride, pooling — feature hierarchy.
• Evolution: LeNet → AlexNet → VGG → Inception → ResNet.
• The magic of skip connections (ResNet).
• Snippet: Minimal Conv2D feature extractor.

Module IV: The Keepers of Sequence — RNNs

• From vanilla RNNs to LSTM/GRU,solving memory limits.
• Gated architectures, encoder-decoder, teacher forcing.
• Snippet: Tiny LSTM next-token prediction demo.

Module V: Coda Legacy, Limits & Relevance

• When old still wins: low-latency, edge devices, small-data settings
• What they couldn’t do and why we needed attention/Transformers.

What You’ll Master

Architectural Intuition: Understand why each model works, when to use it, and what to avoid.

Bridge to Modern AI: Speak clearly about the trade-offs that led to attention, Transformers, and beyond.