DDS-8150

Vasilev, I. (2023). Chapter 8: Sequence-to-Sequence Models and Attention. In Advanced deep learning with Python: Design and optimize neural network models. Packt Publishing.
This chapter provides a comprehensive guide to using LSTM networks for time series data, focusing on sequential learning.

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27–48.

Karpathy, A., Johnson, J., & Fei-Fei, L. (2015). Visualizing and understanding recurrent networks. arXiv preprint arXiv:1506.02078.

Chollet, F. (2022). Chapter 10: Deep Learning for Time Series. In Deep learning with Python (2nd ed.). Manning Publications.
This chapter offers a detailed introduction to CNNs, which are essential for computer vision tasks.

Weidman, S. (2019). Deep learning from scratch. O'Reilly Media, Inc.
This chapter offers a detailed introduction to CNNs, which are essential for computer vision tasks.

Howard, J., & Gugger, S. (2022). Chapter 7: Training a state-of-the-art CNN. In Deep learning for coders with fastai and PyTorch, Revised Edition. O’Reilly Media.
This chapter walks through building and training CNNs using the fastai and PyTorch libraries, focusing on practical computer vision applications.

Shi, X., Lv, F., Seng, D., Zhang, J., Chen, J., & Xing, B. (2021). Visualizing and understanding graph convolutional network. Multimedia Tools and Applications, 80, 8355–8375.
This article provides an overview of CNNs.