Pinned Repositories
100-Days-Of-ML-Code
100 Days of ML Coding
2016-10-facebook-fact-check
Data and analysis for the BuzzFeed News article, "Hyperpartisan Facebook Pages Are Publishing False And Misleading Information At An Alarming Rate."
awesome-algorithm
Leetcode 题解 (跟随思路一步一步撸出代码) 及经典算法实现
Awesome-pytorch-list
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
awesome_deep_learning_interpretability
深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)
BayesByHypernet
Code for the paper Implicit Weight Uncertainty in Neural Networks
bayesian-machine-learning
Notebooks related to Bayesian methods for machine learning
Bayesian-Neural-Networks
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace and more
deep-Bayesian-nonparametrics-papers
The collection of papers about combining deep learning and Bayesian nonparametrics
deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
zhong2024's Repositories
zhong2024/blog-posts
This repository hosts all the blog posts on machine learning, deep learning and statistical modeling.
zhong2024/Deep-Learning-Book-Chapter-Summaries
Attempting to make the Deep Learning Book easier to understand.
zhong2024/python-machine-learning-book-2nd-edition
The "Python Machine Learning (2nd edition)" book code repository and info resource
zhong2024/BayesByHypernet
Code for the paper Implicit Weight Uncertainty in Neural Networks
zhong2024/python_machine_learning
it contains all my python demo code accompanying my machine learning notes
zhong2024/UncertaintyNN
Implementation and evaluation of different approaches to get uncertainty in neural networks
zhong2024/TensorFlow-Tutorials
Simple tutorials using Google's TensorFlow Framework
zhong2024/2016-10-facebook-fact-check
Data and analysis for the BuzzFeed News article, "Hyperpartisan Facebook Pages Are Publishing False And Misleading Information At An Alarming Rate."