How To make Approximate Equivalents
MEASURE EQUIVALENTS:
3 ts 1 tb
16 tb 1 c
1/4 c 4 tb
1/3 c 5-1/3 tb
2 c 1 pt
4 c 1 qt
2 pt 1 qt
1 1/2 fl 1 jigger
WEIGHT EQUIVALENTS:
1 oz 25 gm
1/16 oz 1 gm; 0.035 oz
1 oz 28.35 gm
1 lb 453.6 grams
2 1/4 lb 1 kg
PRODUCT EQUIVALENTS:
3 1/2 c 1 lb brown sugar
2 1/4 c 1 lb granulated sugar
3 3/4 c 1 lb powdered sugar
2 c 1 lb butter
2 c 1 lb shortening
4 1/2 c 1 lb cheese; grated
3 3/4 c 1 lb flour
3 1/3 c 1 lb whole wheat flour
3 1/4 c 1 lb corn meal
3 c 1 lb raisins, seeded
2 2/3 c 1 lb dates, pitted
3 1/2 c 1 lb dates, unpitted
CAN EQUIVALENTS:
1 1/2 c #1 can
2 1/2 c #2 can
3 1/2 c #2-1/2 can
4 c #3 can
13 c #10 can
OTHER EQUIVALENTS:
4 1/2 c 3 lb chicken, cooked/diced
-(1-1/2 lb) 2 tb Cocoa = 1 chocolate square
1 c Uncooked macaroni = 2-2/3
-c cooked 1 lb Uncooked meat = 2-2/3 cooked
1 c Uncooked rice = 4 c cooked
1 c Uncooked spaghetti=2c cooked
How To make Approximate Equivalents's Videos
Alexander Pushnitski : Rational approximation of functions with logarithmic singularities
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: And discover all its functionalities:
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Abstract: I will report on the results of my recent work with Dmitri Yafaev (Rennes I). We consider functions ω on the unit circle with a finite number of logarithmic singularities. We study the approximation of ω by rational functions in the BMO norm. We find the leading term of the asymptotics of the distance in the BMO norm between ω and the set of rational functions of degree nn as nn goes to infinity. Our approach relies on the Adamyan-Arov-Krein theorem and on the study of the asymptotic behaviour of singular values of Hankel operators. In particular, we make use of the localisation principle, which allows us to combine the contributions of several singularities in one asymptotic formula.
Recording during the Annual conference of the functional analysis, harmonic analysis and probability Gdr research group the December 2, 2015 at the Centre International de Rencontres Mathématiques (Marseille, France)
Filmmaker: Guillaume Hennenfent
Approximate nearest neighbors and vector models, introduction to Annoy
Vector models are being used in a lot of different fields: natural language processing, recommender systems, computer vision, and other things. They are fast and convenient and are often state of the art in terms of accuracy. One of the challenges with vector models is that as the number of dimensions increase, finding similar items gets challenging. Erik developed a library called Annoy that uses a forest of random tree to do fast approximate nearest neighbor queries in high dimensional spaces. We will cover some specific applications of vector models with and how Annoy works.
Speaker Bio:
Erik Bernhardsson is the CTO at Better, a small startup in NYC working with mortgages. Before Better, he spent five years at Spotify managing teams working with machine learning and data analytics, in particular music recommendations.
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PGT Agar Cups Recipe (Ez as 123)
Today's class is an introduction to Agar, what the benefits of using Agar are, and a recipe for how I make my agar cups that you can make at home using ingredients found at your local grocery stores.
Agar Agar -
Potato Flakes -
Corn Syrup -
2oz Ketchup Cups -
Media Bottle -
Digital Gram Scale -
Laser Thermometer Gun -
Mirro Pressure Cooker -
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CCN 2019: Tutorial T-C: Approximate inference in the brain: free energy, sampling, and beyond
2019 Conference on Cognitive Computational Neuroscience
13-16 September 2019, Berlin, Germany
Tutorial T-C Approximate inference in the brain: free energy, sampling, and beyond
Presented by Sam Gershman
UC Irvine CEE-290: Topic 7 (Approximate Bayesian Computation)
Topics that will be addressed include:
1. What is diagnostic model evaluation?
2. Why diagnostic model evaluation?
3. Classical likelihood functions mix and dilute information
4. Medical diagnostics
5. Back pain
6. The diagnostic approach
7. Likelihood free inference
8. Approximate Bayesian computation
9. Approximate Bayesian computation with summary statistcs
10. How to select value for epsilon?
11. How to sample ABC posterior distribution?
12. Rejection sampling
13. Population Monte Carlo sampling
14. Markov chain Monte Carlo simulation with DREAM_(ABC)
15. Benchmark studies PMC - DREAM_(ABC)
16. Case study: diagnostic model evaluation
17. Runoff index
18. Recession analysis
19. Flow duration curve: Closed-form equation
20. Byproduct: new method for geophysical inversion
21. Byproduct: new method to help detect system nonstationarity