Shaoqi's Projects
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Code collection for published blog posts
This is a place where I store my scripts used in Coursera
Some up-to-date results of applying deep learning techniques in physics and some resources collected for learning how to leverage deep learning to solve problems in physics
Keras code and weights files for popular deep learning models.
Experiments for understanding disentanglement in VAE latent representations
Exercises and additional information for the lecture dynamical systems in biology at Uni Freiburg
read books and add some comments
Notebooks and code for the book "Introduction to Machine Learning with Python"
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
template repo for field work research data
Minimal is a Jekyll theme for GitHub Pages
This course covers how you can use NLP to do stuff.
萌典網站
Image-to-image translation with conditional adversarial nets
Repository for Programming Assignment 2 for R Programming on Coursera
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Starmind application task, finding names close to 'Luca' in terms of levenshtein diatance
some R codes leart in Coursera for analysing time series data
Performing sentiment analysis of tweets including target stock and company in real time, then visualizing the trend
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow