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GPT stands for generative pretrained transformers. It is a type of language model which has been trained on...

DevOps, DataOps, MLOps Duke University If you are kinda fed up with training models and would like to...

You probably have known the DevOps methodology which is very commonly used in the software industry, which basically...

Python Essentials for MLOps Duke University You will learn a lot from this course: Python language itself, PyTest...

Fitting Statistical Models to Data with Python University of Michigan AI is combination of many different fields. Statistics...

Logistic Regression Logistic regression is an extension to linear regression. When there are only only 2 options for...

The purpose of fitting models to data is to help answer research questions. We specify models based on...

Typically we have some question, and that question could be “could the value of the parameter be X?”...

We use data to do statistical inference means we either estimate some parameters with confidence, or test some...

TensorFlow 2 for Deep Learning Specialization Imperial College London Hooray! Now I completed the whole specialization! TensorFlow skill...

Probabilistic Deep Learning with TensorFlow 2Imperial College London The focus of this course is the TensorFlow Probability library....

The Variational Autoencoder (VAE) is an algorithm for inference and learning in a latent variable generative model. In...

Data for Machine LearningAlberta Machine Intelligence Institute Do you agree that the skills of manipulating data is more...

Generative models are a kind of statistical model that aims to learn the underlying data distribution itself. If a generative...

Unfortunately, deep learning models aren’t always accurate, especially when asked to make predictions on new data points that are dissimilar to the...

Andrew Ng Anna Koop Brenda Gunderson Computer Communications Specialization Cryptography Differential Equations for Engineers Economics of Money and Banking Evgenii Vashukevich Foundations of Quantum Mechanics Garud Iyengar Ivan Vybornyi Jeffrey Chasnov John Daily Jonathan Katz Kevin Webster Ling-Chieh Kung Machine Learning: Algorithms in the Real World Martin Haugh Mathematics for Engineers Specialization Matthew Hutchens Michael Donohoe Michael Fricke Microsoft Azure Fundamentals AZ-900 Exam Prep Specialization Operations Research (3): Theory Perry Mehrling Petro Lisowsky Physical Basics of Quantum Computing Practical Reinforcement Learning Rebekah May Search Engine Optimization (SEO) Specialization Sergey Sysoev Statistical Thermodynamics Specialization Statistics with Python Specialization Taxation of Business Entities I: Corporations TensorFlow 2 for Deep Learning Specialization U.S. Federal Taxation Specialization Understanding Modern Physics I: Relativity and Cosmology Wounjhang Park Xiaobo Zhou Yi Wang

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