![]() MATLAB and Simulink provide a flexible and powerful platform to develop and automate data analysis, deep learning, AI, and simulation workflows in a wide range of domains and industries. It’s becoming essential for students and educators to adopt this technology to solve complex real-world problems. Materials for the Deep Learning with MATLAB Sessionĭeep learning is quickly becoming embedded in everyday applications. Building and deploying interactive applications Day 2: Deep Learning with MATLAB – Hands-on Workshop Sharing your results with others by automatically creating reportsĥ. Automating and capturing your work in easy-to-write scripts and programsĤ. Using interactive tools for iterative exploration, design, and problem-solvingģ. Accessing data from many sources (files, other software, hardware, etc.)Ģ. However, experienced MATLAB users may also benefit from the session, as the presenter, Evan Cosgrave (Ph.D.), will be covering some tips and tricks from the newer releases of MATLAB:ġ. This session is targeted at those who are new to MATLAB. ![]() Through live demonstrations and examples, you will see how MATLAB can help you become more effective in your work. ![]() In this session, you will learn how MATLAB can be used to visualize and analyze data, perform numerical computations, and develop algorithms. Materials for the Introduction to MATLAB Session It includes a short MATLAB introduction w.r.t. The simplest solution would be to round your numbers yourself (unless you can use something like decimal.Decimal, but this means you should forgo native doubles entirely, including literals) and reproduce MATLAB's mod that way, assuming that makes sense for your use cases.At FAU MoD Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg, we are glad to invite you all to the free (online) two-day course on April 28-29th, 2022 about Deep Learning with MATLAB organized by MathWorks in cooperation with FAU MoD and CMAI (Fairfax, Virginia). So MATLAB goes out of its way to do some magic with the floating-point results. This suggests that when x/y is close to an integer then it's rounded first, rather than being truncated like in python. Within roundoff error of an integer, then n is that integer. If y is not an integer and the quotient x./y is Now here's help mod from MATLAB: MOD(x,y) returns x - floor(x./y).*y if y ~= 0, carefully computed toĪvoid rounding error. One can easily prove that these two numbers are indeed the same within double precision: > print(6/Decimal('0.05') - 6/Decimal(0.05)) The first number is what you'd first get with 6/0.05, but the number 119.9999999999999933386618522 gets rounded to the nearest number representable with double precision, and this is 120. ![]() print(6/Decimal(0.05)) # exactly approximate Some proof: > from decimal import Decimal However, it's ever so slightly smaller than 120, so explicit floor division will truncate that number to 119 before it could be normalized to 120.0. within the resolution of double precision) that it gets rounded to 120.0. The floating-point result of 6/0.05 is close enough to 120 (i.e. This is the core of the problem, in python: > 6/0.05 = 120
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