{"repo":"ctgk/PRML","free":true,"listed":false,"github":"https://github.com/ctgk/PRML","clone":"git clone https://github.com/ctgk/PRML.git","description":"PRML algorithms implemented in Python","language":"Jupyter Notebook","stars":11724,"topics":["jupyter","prml","notebook","python"],"license":"MIT","category":"machine-learning","readme_excerpt":"PRML Python codes implementing algorithms described in Bishop's book \"Pattern Recognition and Machine Learning\" Required Packages - python 3 - numpy - scipy - jupyter (optional: to run jupyter notebooks) - matplotlib (optional: to plot results in the notebooks) - sklearn (optional: to fetch data) Notebooks The notebooks in this repository can be viewed with nbviewer or other tools, or you can use Amazon SageMaker Studio Lab, a free computing environment on AWS (prior registration with an email address is required. Please refer to this document for usage). From the table below, you can open the notebooks for each chapter in each of these environments. nbviewer Amazon SageMaker Studio Lab :------- :--------------------------: ch1. Introduction ch2. Probability Distributions ch3. Linear Models for Regression ch4. Linear Models for Classification ch5. Neural Networks ch6. Kernel Methods ch7. Sparse Kernel Machines ch8. Graphical Models ch9. Mixture Models and EM ch10. Approximate Inference ch11. Sampling Methods ch12. Continuous Latent Variables ch13. Sequential Data If you use the SageMaker Studio Lab, open a terminal and execute the following commands to install the required libraries.","default_branch":null,"files":null,"tree":[],"storefront":"/r/ctgk","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ctgk/PRML/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}