zhengqigao / PRML-Solution-ManualLinks
My Own Solution Manual of PRML
☆988Updated 4 years ago
Alternatives and similar repositories for PRML-Solution-Manual
Users that are interested in PRML-Solution-Manual are comparing it to the libraries listed below
Sorting:
- My solutions to Kevin Murphy Machine Learning Book☆541Updated 4 years ago
- Practical assignments of the Deep|Bayes summer school 2019☆832Updated 5 years ago
- 🌀 Stanford CS 228 - Probabilistic Graphical Models☆122Updated 6 years ago
- Course notes☆704Updated last year
- Statistical Learning Theory (CS229T) Lecture Notes☆720Updated 5 years ago
- ☆812Updated 3 months ago
- Solutions to "Machine Learning: A Probabilistic Perspective"☆162Updated 4 years ago
- Course notes for CS228: Probabilistic Graphical Models.☆1,965Updated last month
- EPFL Course - Optimization for Machine Learning - CS-439☆1,306Updated 3 weeks ago
- Topics course Mathematics of Deep Learning, NYU, Spring 18☆545Updated 2 years ago
- ☆231Updated 2 years ago
- ☆278Updated 2 years ago
- 🌲 Stanford CS 228 - Probabilistic Graphical Models☆136Updated 11 months ago
- Python implementations (on jupyter notebook) of algorithms described in the book "PRML"☆255Updated 4 years ago
- 🖥️ CS446: Machine Learning in Spring 2018, University of Illinois at Urbana-Champaign☆283Updated 6 years ago
- EE227C (Spring 2018) Course page☆223Updated 4 years ago
- Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)☆2,814Updated 2 years ago
- Seminars DeepBayes Summer School 2018☆1,045Updated 5 years ago
- PyTorch for Numpy users. https://pytorch-for-numpy-users.wkentaro.com☆705Updated 2 years ago
- Notebooks about Bayesian methods for machine learning☆1,872Updated last year
- ☆175Updated 6 years ago
- This introduces a suggestion of mathematical notation protocol for machine learning.☆474Updated 11 months ago
- Repository of my solutions to the problems of "Learning from Data"☆274Updated 5 years ago
- Normalizing flows in PyTorch. Current intended use is education not production.☆874Updated 5 years ago
- Machine learning course materials.☆573Updated last year
- My solutions to problems of The Elements of Statistical Learning by Profs. Hastie, Tibshirani, and Friedman.☆90Updated 6 years ago
- A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.☆821Updated 4 years ago
- lecture notes of "Matrix Methods in Data Analysis, Signal Processing, and Machine Learning"☆150Updated 5 years ago
- Quick, visual, principled introduction to pytorch code through five colab notebooks.☆431Updated 6 months ago
- Stanford CS229 (Autumn 2017)☆366Updated 7 years ago