numexpr - Fast numerical array expression evaluator for Python and NumPy. Please be aware that the numexpr project has been migrated to GitHub. This site has been declared unmaintained as of 2014-01-21. Sorry for the inconveniences. -- Francesc Alted What It Is The numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. Also,numexpr implements support for multi-threading computations straight into its internal virtual machine, written in C. It is also interesting to note that, as of version 2.0, numexpr uses the new iterator introduced in NumPy 1.6 so as to achieve better performance in a broader range of data arrangements. Finally, numexpr has support for the Intel VML (Vector Math Library) -- integrated in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing transcendental functions (like trigonometrical, exponentials...) on top of Intel-compatible platforms. Examples of Use Using it is simple: >>> import numpy as np>>> import numexpr as ne >>> a = np.arange(1e6)>>> b = np.arange(1e6) and fast...
Online texts Professor Jim Herod and I have written Multivariable Calculus ,a book which we and a few others have used here at Georgia Tech for two years. We have also proposed that this be the first calculus course in the curriculum here, but that is another story.... Although it is still in print, Calculus,by Gilbert Strang is made available through MIT's OpenCourseWare electronic publishing initiative. Here is one that has also been used here at Georgia Tech. Linear Methods of Applied Mathematics, by Evans Harrell and James Herod. Sage - French
Math Alive Course Instructors Ingrid DaubechiesShannon Hughes 218 (ID)/217 (SH) Fine Hall, Washington Road Princeton, NJ 08540-1000 You can find other contact information on a Contact us page. How is life different from 25 or even 10 years ago? Cryptography Error correction & compression Probability & Statistics Birth, Growth, Death & Chaos Graph Theory Voting & Social Choice You can navigate through the units using the navigation bar on the left. Each unit is divided into two parts. Don't forget to check the latest announcements on the Announcements page. Problem Sets: You need to look at the On-Line Labs and at the Problem Sets (clickable on the left). The videotaped lectures of Spring 2003 course are available on blackboard. References: For All Practical Purposes : Introduction to Contemporary Mathematics. 4th edition (December 1997). If you have any questions, Send Mail To: Math Alive Help
SymPy Gaussianos Welcome — Theano 0.6 documentation Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.transparent use of a GPU – Perform data-intensive computations much faster than on a CPU.efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.dynamic C code generation – Evaluate expressions faster.extensive unit-testing and self-verification – Detect and diagnose many types of errors. Theano has been powering large-scale computationally intensive scientific investigations since 2007. 2017/11/15: Release of Theano 1.0.0. You can watch a quick (20 minute) introduction to Theano given as a talk at SciPy 2010 via streaming (or downloaded) video: git clone How to Seek Help¶
No, really, pi is wrong: The Tau Manifesto by Michael Hartl | Tau Day, 2010