My personal collection of Machine Learning/Deep Learning and Algorrithms
Link: https://digitalcommons.usf.edu/etd/8442/
Main Objectives:
- Learn how to write and edit an academic paper
- Practice designing a machine learning project with clear objectives
- Modify and develop algorithms in an open-source ML software, Weka (Java)
1. Jing Lin, Long Dang, Mohamed Rahouti, Kaiqi Xiong: ML Attack Models: Adversarial Attacks and Data Poisoning Attacks In-vehicle network security (Sep 2021)
2. Dang, Long, Thushari Hapuarachchi, Kaiqi Xiong, and Jing Lin. "Improving Machine Learning Robustness via Adversarial Training." (May-2023)
In 2023 32nd International Conference on Computer Communications and Networks (ICCCN), pp. 1-10. IEEE, 2023. Link: https://ieeexplore.ieee.org/document/10230138
Please refer to this Github link.
Github: https://github.com/saigontrade88/Coursera_Deep_Learning
Course's Website: https://www.coursera.org/specializations/deep-learning#courses
- Course 1: Neural Networks and Deep Learning (completed). Please refer to the Github link.
- Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (ongoing, completed week 1 out of 3). Please refer to the Github link.
- Course 3: Structuring Machine Learning Projects (not started yet)
- Course 4: Convolutional Neural Networks (ongoing, completed weeks 1, and 2 out of 4). Please refer to the Github link.
- Course 5: Sequence Models (not started it yet)
Courses's website: https://www.coursera.org/specializations/algorithms
- Course 1: Divide and Conquer, Sorting and Searching, and Randomized Algorithms (Completed with certification).
- Course 2: Graph Search, Shortest Paths, and Data Structures
- Course 3: Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
- Course 4: Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
Github: https://github.com/saigontrade88/Algo_Coursera_Princeton
Course's website: https://www.coursera.org/learn/algorithms-part1
Courses's website: https://www.coursera.org/specializations/mathematics-machine-learning
- Course 1: Mathematics for Machine Learning: Linear Algebra (Completed with certification).
- Course 2: Mathematics for Machine Learning: Multivariate Calculus (ongoing)
- Course 3: Mathematics for Machine Learning: PCA
8. Youtube. Introduction to machine learning for pattern classification, regression analysis, clustering, and dimensionality reduction.
Link: https://www.youtube.com/playlist?list=PLTKMiZHVd_2KyGirGEvKlniaWeLOHhUF3
Reference book by the author Machine learning with pytorch and scikit-learn : develop machine learning and deep learning models with python Authors: Raschka, Sebastian, author.; Liu, Yuxi (Data scientist), author.; Mirjalili, Vahid, author. Birmingham, England : Packt Publishing; 2022 Free PDF Book on the USF online library at https://usf-flvc.primo.exlibrisgroup.com/permalink/01FALSC_USF/8i1ivu/alma99380179727706599
Link: https://www.youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51
Link: https://course.fast.ai/ Reference book Howard, Jeremy, and Sylvain Gugger. Deep Learning for Coders with fastai and PyTorch. O'Reilly Media, 2020.
The physical book is available from 1) USF’s ILL service The physical book and PDF can be obtained from Hilsborough county's local library system at https://hcplc.bibliocommons.com/v2/search?searchType=smart&query=deep%20learning%20for%20coders%20with%20fastai%20and%20pytorch