Deep Learning Roadmap
What is deep learning?
ML with neural networks that have multiple layers. “Deep” = many layers. Can learn complex patterns directly from raw data (images, text, audio) without manual feature engineering.
When deep learning vs classical ML
| Classical ML | Deep Learning |
|---|---|
| Tabular data | Images, text, audio, video |
| Small/medium data | Large data (thousands+) |
| Interpretability matters | Performance matters |
| Quick iteration | GPU available |
| Feature engineering is feasible | Features are hard to hand-craft |
Topics
Foundations
- Neurons and Activation Functions — the building blocks
- Backpropagation — how neural nets learn
- Optimizers — Adam, SGD, and beyond
- Vanishing and Exploding Gradients — why deep nets are hard to train
- Batch Normalization — stabilizing training
Architectures
- Convolutional Neural Networks — images, spatial data
- Recurrent Neural Networks — sequences (mostly historical now)
- Transformers — the dominant architecture for text, increasingly everything
- Attention Mechanism — the core innovation behind transformers
- Autoencoders — compression, denoising, generation
Training
- Transfer Learning — reuse pretrained models (the practical default)
- Data Augmentation — artificially expand training data
Learning order
- Neurons + Activation Functions → understand single layer
- Backpropagation → understand how training works
- Build a simple feedforward net in PyTorch → hands on
- CNNs → image classification project
- Transformers + Attention → the modern foundation
- Transfer Learning → practical deep learning workflow
Links
- Machine Learning Roadmap — classical ML foundations
- PyTorch Essentials — the framework
- NLP Roadmap — text applications
- Computer Vision Roadmap — image applications
See Also
- Deep Residual Learning for Image Recognition — ResNet (He et al., 2015). Skip connections enabled training of 100+ layer networks, solving vanishing gradients and winning ImageNet.
- Learning Representations by Back-Propagating Errors — Rumelhart et al. (1986). The original backpropagation paper that introduced the algorithm still used today for training neural networks.