Pons, An Async Client For Ethereum | PyChain 2022
This is a video recording of the PyChain 2022 conference sessions. Speaker: Bogdan Opanchuk - Entropy Cryptography Pons as an alternative for Web3.py Web3.py has been a go-to Ethereum client in Python for some time, and it works well enough for most users. But in some cases, an alternat
From playlist PyChain 2022
Using Sympy to solve algebraic expressions and equations.
From playlist Introduction to Pyhton for mathematical programming
Extending the capabilities of Python with the Math module
From playlist Introduction to Pyhton for mathematical programming
SDS 585: PyMC for Bayesian Statistics in Python — with Thomas Wiecki
#PyMC #BayesianStatistics #Python In this episode, Dr. Thomas Wiecki, Core Developer of the PyMC Library and CEO of PyMC Labs, joins Jon for a masterclass in Bayesian statistics. Tune in to hear PyMC, and discover why Bayesian statistics can be more powerful and interpretable than any oth
From playlist Super Data Science Podcast
Various manipulations of matrices including teh caclulation of eigenvalues and eigenvectors.
From playlist Introduction to Pyhton for mathematical programming
From playlist Miscellaneous
102 Printing mathematical symbols in Sympy
How to output your mathematical code using an in built Sympy printer.
From playlist Introduction to Pyhton for mathematical programming
Setting up and solving ordinary differential equations with constant coefficients.
From playlist Introduction to Pyhton for mathematical programming
Dan Foreman-Mackey - Methods for scalable probabilistic inference - IPAM at UCLA
Recorded 17 November 2021. Dan Foreman-Mackey of the Flatiron Institute presents "Methods for scalable probabilistic inference" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy. Abstract: Most data analysis pipelines in astrophysics now have
From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy
Why Do I Need to Know Python -- I'm a Pandas User || James Powell
It's common for data scientists to narrowly focus on the APIs of the tools they use every day—pandas, matplotlib, pymc, dask, &c.—to the detriment of any focus on the surrounding programming language. In the case of tools like matplotlib, the total amount of Python we need to know is limit
From playlist Python
Probabilistic Python: An Introduction to Bayesian Modeling with PyM || Chris Fonnesbeck
Bayesian statistical methods offer a powerful set of tools to tackle a wide variety of data science problems. In addition, the Bayesian approach generates results that are easy to interpret and automatically account for uncertainty in quantities that we wish to estimate and predict. Histor
From playlist Python
A brief introduction to Python. Where to go to download Python and what to install.
From playlist Introduction to Pyhton for mathematical programming
Statistical Rethinking 2022 Lecture 19 - Generalized Linear Madness
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Square hole: https://www.youtube.com/watch?v=9nSQs0Gr9FA Music: https://www.youtube.com/watch?v=Ntv9R1She5A Pause: https://www.youtube.com/watch?v=pxPdsqrQByM Music: https://www.youtube.com/watch?v=D_cOo
From playlist Statistical Rethinking 2022
Using the Pythagorean identity to verify an identity
👉 Learn how to verify Pythagoras trigonometric identities. A Pythagoras trigonometric identity is a trigonometric identity of the form sin^2 (x) + cos^2 (x) or any of its derivations. To verify trigonometric expression means to verify that the term(s) on the left-hand side of the equality
From playlist Verify Trigonometric Identities