My 136th certificate from Coursera
DevOps, DataOps, MLOps Duke University If you are kinda fed up with training models and would like to...
Introduction to MLOps
You probably have known the DevOps methodology which is very commonly used in the software industry, which basically...
My 135th certificate from Coursera
Python Essentials for MLOps Duke University You will learn a lot from this course: Python language itself, PyTest...
My 133rd certificate from Coursera
Fitting Statistical Models to Data with Python University of Michigan AI is combination of many different fields. Statistics...
Multilevel Models vs Marginal Models
Logistic Regression Logistic regression is an extension to linear regression. When there are only only 2 options for...
Fitting Parametric Models to Data
The purpose of fitting models to data is to help answer research questions. We specify models based on...
Hypothesis Testing
Typically we have some question, and that question could be “could the value of the parameter be X?”...
Confidence Intervals
We use data to do statistical inference means we either estimate some parameters with confidence, or test some...
My #8 specialization certificate from Coursera
TensorFlow 2 for Deep Learning Specialization Imperial College London Hooray! Now I completed the whole specialization! TensorFlow skill...
My #118 certificate from Coursera
Probabilistic Deep Learning with TensorFlow 2Imperial College London The focus of this course is the TensorFlow Probability library....
Variational Autoencoders
The Variational Autoencoder (VAE) is an algorithm for inference and learning in a latent variable generative model. In...
My #114 certificate from Coursera
Data for Machine LearningAlberta Machine Intelligence Institute Do you agree that the skills of manipulating data is more...
TensorFlow: Normalizing Flow Models
Generative models are a kind of statistical model that aims to learn the underlying data distribution itself. If a generative...
TensorFlow: Probabilistic Deep Learning Models
Unfortunately, deep learning models aren’t always accurate, especially when asked to make predictions on new data points that are dissimilar to the...
Distribution Objects in TensorFlow Probability
We’ll be making extensive use of the TensorFlow Probability library to help us develop probabilistic deep learning models. The...