1  Syllabus

1.1 Content by week

Week	Content	Notes
1	Introduction to Neural Networks and LLM
2	Single-Layer Perceptrons
3	Multi-Layer Perceptrons
4	Torch and autograd, Activation functions
5	Loss functions, convexity, gradient descent, optimizers
6	Recurrent Neural Networks: GRU, LSTM
7	Attention Mechanisms and Transformers
8	Encoding
9	Embedding
10	GPT Model
11	Pre training
12	Fine tuning Classification
13	Fine tuning To Follow Instructions
14	Review other models GPT4, Deepseek

1.2 Books

• LLM From Scratch
• Deep Learning with Python, Third Edition

1.3 Exams

• %40 Midterms
• %60 Finals