Background Gaussian sampling — that is, generating samples from a Gaussian distribution — plays an important role in many cutting-edge fields of data science, such as Gaussian process, variational autoencoder, or generative adversarial network. As a result, you often see functions like tf.random.normal in their tutorials. But, deep down, how does computer know how to generate Gaussian samples? This series …
At the moment, AI/ML/DL are hot keywords in the trend of Software development. The world have more successful projects based on AI technologies such as Google Translate, AWS Alexa, …AI makes machine smarter than. So, the way from idea to successfully have many challenges if want to make great solutions. I have some time working …
Background In previous parts of my project, I built different n-gram models to predict the probability of each word in a given text. This probability is estimated using an n-gram — a sequence of words of length n — which contains the word. The below formula shows how the probability of the word “dream” is estimated …
Introduction Optimization shows up everywhere in machine learning, from the ubiquitous gradient descent to quadratic programming in SVM, to expectation-maximization algorithm in Gaussian mixture models. However, one aspect of optimization that always puzzled me is duality: what on earth are a primal form and dual form of an optimization problem, and what good do they serve? Therefore, in this project, I will: Go …
