Welcome to the DS5006 - Deep Learning repository! This repository contains course materials, including slides, lecture notes, assignments, projects, recommended books, reference books, research papers, and datasets.
This section contains slides and presentation materials used in the DS5006 Deep Learning course. You can find the slides organized by topics and lectures. Feel free to browse and download them for your reference.
Here, you will find lecture notes and supplementary materials for each lecture in the DS5006 course. These resources provide in-depth explanations, code examples, and additional references to support your learning journey.
This section includes assignments given during the DS5006 course. Each assignment may come with instructions, starter code, and datasets, where applicable. You can find solutions and submissions related to these assignments.
The projects section features information about course projects, project guidelines, and resources to help you work on your deep learning projects. You can also find project examples and previous student submissions for inspiration.
For foundational learning, recommend the following books:
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Deep Learning for Coders with fastai and PyTorch by Jeremy Howard & Sylvain Gugger
- Dive into Deep Learning old by Aston Zhang, Zachary C. Lipton, MU LI, and Alexander J. Smola
- Dive into Deep Learning new by Aston Zhang, Zachary C. Lipton, MU LI, and Alexander J. Smola
- Neural Networks & Deep Learning by Michael Nielsen
- Neural Networks and Deep Learning by Charu C. Aggarwal
- Neural Networks for Pattern Recognition by Christopher M. Bishop
- Neural Networks from Scratch in Python by Harrison Kinsley & Daniel Kukieła
- Python Deep Learning by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
- Feature Engineering for Machine Learning Principles and Techniques for Data Scientists by Alice Zheng & Amanda Casari
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron
- Introduction to Machine Learning with Python A Guide for Data Scientists by Andreas C. Müller & Sarah Guido
- Machine Learning by Tom M. Mitchell
- Machine Learning With Python by Bernd Klein
- Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 by Sebastian Raschka & Vahid Mirjalili
- Python Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 by Sebastian Raschka & Vahid Mirjalili
These reference books provide advanced insights into deep learning:
- Advanced Deep Learning with TensorFlow 2 and Keras by Rowel Atienza
- Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
- ...
Explore the latest research papers in deep learning:
Here, you can access datasets that are used in the course for hands-on exercises and projects. We provide links and instructions on how to download and use these datasets effectively.
Feel free to explore the repository, access the materials you need, and contribute if you have valuable resources to share with fellow students.