# Neural-network foundations

Subject: Deep Learning

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/#tee/guide/4

Canonical: https://deep-learning-study-room.vercel.app/read/dl/tee/guide/4/

1. [What does learning from data mean](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s0/)
2. [Your first complete training step](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s1/)
3. [Classification regression and learning types](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s2/)
4. [Training validation and test data](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s3/)
5. [Parameters and hyperparameters](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s4/)
6. [Artificial intelligence machine learning and deep learning](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s5/)
7. [What one artificial neuron does](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s6/)
8. [Model answer: explain an artificial neuron](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s7/)
9. [Perceptrons and the threshold](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s8/)
10. [Why XOR needs more than one perceptron](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s9/)
11. [Activation functions and how to calculate them](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s10/)
12. [Model answer: why a network needs nonlinear activation](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s11/)
13. [Choosing a loss](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s12/)
14. [Forward propagation through multiple neurons](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s13/)
15. [Backpropagation with one hidden neuron](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s14/)
16. [More outputs and vectorized backpropagation](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s15/)
17. [Model answer: forward propagation, backpropagation and updating](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s16/)
18. [Counting parameters in a dense network](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s17/)
19. [What you should understand before optimization](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s18/)
20. [Complete sigmoid training example](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s19/)
21. [Cross-entropy for probability distributions](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s20/)
22. [Biological neurons and the McCulloch-Pitts model](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s21/)