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
Proportions | Solving Proportions with Variables
Welcome to Solving Proportions with Variables with Mr. J! Need help with how to solve proportions? You're in the right place!
Whether you're just starting out, or need a quick refresher, this is the video for you if you need help with solving proportions (using cross multiplication). Mr. J will go through solving proportions examples and explain the steps of how to solve a proportion with a variable using cross multiplication.
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Music:
Hopefully this video is what you're looking for when it comes to solving proportions.
Have a great rest of your day and thanks again for watching!
How to use the HLOOKUP function in Excel
Learn how to use the HLOOKUP function in Microsoft Excel. This tutorial demonstrates how to use Excel HLOOKUP with an easy to follow example and takes you step-by-step through the different options when entering your formula.
HLOOKUP Function (and sample data):
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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
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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A tutorial on Quantum Approximate Optimization Algorithm (Oct 2020). Part 1: Theory
[UPD] A new and slightly improved version of this tutorial is available here:
Part 1 of the tutorial on Combinatorial Optimization on Quantum Computers.
The slides and the Jupyter notebooks for the hands-on session can be downloaded here:
0:00 Intro
0:55 Part 0: Big picture considerations
7:03 Part 1: Mapping combinatorial optimization problems onto quantum computers
21:03 Part 1.1: Mapping arbitrary binary functions
28:50 Part 2: Quantum Approximate Optimization Algorithm (QAOA)
37:26 Part 2.1: Connection between QAOA and adiabatic quantum optimization
44:20 Part 2.2: Training QAOA purely classically
50:15 Conclusion
How To Solve Approximate Annual Interest Rate Questions for BEC Portion of CPA Exam
When I took the exam in late 2022, I did NOT receive this type of question on the exam, nor did I receive any similar questions. I found the actual questions were more simple and straightforward than this. However, this problem and video are left up for informational purposes.