Neural Nets·Course
Neural Networks & Deep Learning
Neural networks: perceptron, backpropagation, CNNs and RNNs, transformers, and modern architectures
5
Modules
15
Articles
~2 h
Reading
IV
CLOs
§ 01 — Curriculum
5 modules.
Each module is a small unit. Most read in sequence — but a determined reader can begin anywhere.
- M IFundamentals of Neural NetworksPerceptrons, multilayer networks, and the backpropagation algorithm3 articles
18 minBegin → - M IIDeep Learning: Theory and PracticeOptimization, normalization, and advanced architectures3 articles
18 minBegin → - M IIIRecurrent Neural NetworksLSTM, GRU, sequence modeling, and NLP3 articles
18 minBegin → - M IVGenerative ModelsGANs, VAEs, diffusion models, and data generation3 articles
18 minBegin → - M VTransformers and Large Language ModelsThe attention mechanism, BERT, GPT, and the LLM era3 articles
18 minBegin →
§ 02 — Learning outcomes
4 outcomes.
CLO I
Fundamentals of Neural Networks
Understand neural network architectures, activation functions, and backpropagation
CLO II
CNNs and RNNs
Design and apply convolutional and recurrent architectures
CLO III
Transformers
Understand the attention mechanism and transformer architectures
CLO IV
Generative Models
Work with autoencoders, GANs, and diffusion models
§ 03 — Practices