Probabilistic

ODSC West 2020 Probabilistic Programming and Bayesian Inference with Python

ODSC West 2020 Probabilistic Programming and Bayesian Inference with Python
English | Size: 1.51 GB
Category: Tutorial


If you can write a model in sklearn, you can make the leap to Bayesian inference with PyMC3, a user-friendly intro to probabilistic programming (PP) in Python. PP just means building models where the building blocks are probability distributions! And we can use PP to do Bayesian inference easily. Bayesian inference allows us to solve problems that aren’t otherwise tractable with classical methods.

PluralSight – Quantum Computing Computing With A Probabilistic Universe With Mark Russinovich

PluralSight – Quantum Computing Computing With A Probabilistic Universe With Mark Russinovich-NOLEDGE
English | Size: 281.21 MB
Category: Tutorial


Computing: Computing with a Probabilistic Universe with Mark Russinovich | Mark Russinovich Quantum computing algorithms can solve problems in seconds that would take millions of years on classical computers. Mark describes the kinds of problems that quantum computing accelerates, then introduces quantum physics, the qubits that are the foundation for quantum computing quantum gates, and then algorithms. He uses the Microsoft Quantum Development Kit with its Q# language and simulator to demonstrate concepts and show that even though scalable quantum computers are several years away, quantum inspired algorithms are solving problems more efficiently today

Probabilistic Graphical Model

Probabilistic Graphical Model
English | Size: 2.19 GB
Category: Tutorial

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.