DDS-8150

Chollet, F. (2022). Chapter 2: The mathematical building blocks of neural networks. In Deep learning with Python (2nd ed.). Manning Publications.

Chollet, F. (2022). Chapter 3: Introduction to Keras and Tensorflow. In Deep learning with Python (2nd ed.). Manning Publications.

Chollet, F. (2022). Chapter 4: Getting Started with Neural Networks. In Deep learning with Python (2nd ed.). Manning Publications.
These chapters cover the foundational concepts and mathematics behind neural networks, including layers, weights, and activation functions.

Warner, B., & Misra, M. (1996). Understanding neural networks as statistical tools. In The American Statistician, 50(4), 284–293.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. Chapter 14: Autoencoders. Available at: https://www.deeplearningbook.org/
This chapter provides a hands-on guide to building and training autoencoders using Python and PyTorch, which is essential for understanding unsupervised learning.

Vasilev, I. (2023). Chapter 5: Generative Models. In Advanced deep learning with Python: Design and optimize neural network models. Packt Publishing.
This chapter dives deeper into autoencoder architectures, including advanced types like variational autoencoders (VAEs), providing a more technical look at unsupervised learning.

Liu, W., Li, R., Zheng, M., Karanam, S., Wu, Z., Bhanu, B., ... & Camps, O. (2020). Towards visually explaining variational autoencoders. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8642–8651).