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am205-fall-2014

Class repository for AM205 (Advanced Scientific Computing: Numerical Methods), Fall 2014 at Harvard

annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

bartmachine

An R-Java Bayesian Additive Regression Trees implementation

bda_py_demos

Bayesian Data Analysis demos for Python

cambridgemathematicspartiii

causaltree

Working repository for Causal Tree and extensions

cevae

Causal Effect Inference with Deep Latent-Variable Models

cgnn

Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"

cs228-notes

Course notes for CS228: Probabilistic Graphical Models.

cs228t.2012

The programming assignments from CS228T offered in Spring 2012 at Stanford

cuhk_thesis_template

cvxflow

d2l-en

An interactive deep learning book with code, math, and discussions, based on the NumPy interface.

deeplearningexamples

Deep Learning Examples

deeplearningtutorials

Deep Learning Tutorial notes and code. See the wiki for more info.

drug_repositioning

predict prob of psychiatric disorders using drug expression profile

drug_target

edward

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

fast-lmm

Python version of Factored Spectrally Transformed Linear Mixed Models

hte

insider

kai0511

Config files for my GitHub profile.

learning-to-learn

Learning to Learn in TensorFlow

librec

LibRec: A Leading Java Library for Recommender Systems, see

lihang-code

《统计学习方法》的代码实现

linux_cmds

some commonly used Linux shell commands

mit6.00.2x

programs for MIT6.00.2x

models

Models and examples built with TensorFlow

prml

Pattern recognition and machine learning toolbox

probabilistic-deep-learning-with-tensorflow

Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real-world datasets.