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Articles related to "code"


What is the core of the Python programming language?

  • Python as implemented by CPython is very dynamic and exposes many things that only make sense if you implement Python using an interpreter somehow.
  • The thing that really made me start thinking about this is when I began contemplating what it would take to compile Python down to WebAssembly?
  • But in a compiled language this takes a lot more work as you have to know when to gather all of this information as it isn't necessarily just lying around when one calls locals().
  • I will say that I think WebAssembly doesn't need to support the vast amount of Python software out there to be useful.
  • It might make sense to develop a compiler that translates Python code directly to WebAssembly and sacrifice some compatibility for performance.

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AI Feynman 2.0: Learning Regression Equations From Data

  • Symbolic regression is a way of stringing together the user-specified mathematical functions to build an equation for the output “y” that best fits the provided dataset.
  • We can see from running the code snippet above that the target “y” data points in example1.txt are generated using the equation on line 3, where the inputs are all the columns except for the last one, and the equation generates the last column.
  • The solver runs for a long time, trying different kinds of equations at different levels of complexity, and assessing the best fit for each one.
  • Now that we took a peek at what our data looks like, let’s ask the solver to find a simple equation that fits our data, using our dataset.
  • The idea is that we want the solver to notice that you don’t need all of the supplied variables in order to fit the data.

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AI Feynman 2.0: Learning Regression Equations From Data

  • Symbolic regression is a way of stringing together the user-specified mathematical functions to build an equation for the output “y” that best fits the provided dataset.
  • We can see from running the code snippet above that the target “y” data points in example1.txt are generated using the equation on line 3, where the inputs are all the columns except for the last one, and the equation generates the last column.
  • The solver runs for a long time, trying different kinds of equations at different levels of complexity, and assessing the best fit for each one.
  • Now that we took a peek at what our data looks like, let’s ask the solver to find a simple equation that fits our data, using our dataset.
  • The idea is that we want the solver to notice that you don’t need all of the supplied variables in order to fit the data.

save | comments | report | share on


AI Feynman 2.0: Learning Regression Equations From Data

  • Symbolic regression is a way of stringing together the user-specified mathematical functions to build an equation for the output “y” that best fits the provided dataset.
  • We can see from running the code snippet above that the target “y” data points in example1.txt are generated using the equation on line 3, where the inputs are all the columns except for the last one, and the equation generates the last column.
  • The solver runs for a long time, trying different kinds of equations at different levels of complexity, and assessing the best fit for each one.
  • Now that we took a peek at what our data looks like, let’s ask the solver to find a simple equation that fits our data, using our dataset.
  • The idea is that we want the solver to notice that you don’t need all of the supplied variables in order to fit the data.

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Unscheduled fixes for critical Windows flaws delivered through rare channel

  • Microsoft has published unscheduled fixes for two critical vulnerabilities that make it possible for attackers to execute malicious code on computers running any version of Windows 10.
  • Unlike the vast majority of Windows patches, the ones released on Tuesday were delivered through the Microsoft Store.
  • When I checked both the Microsoft Store and the Windows Update on my Windows 10 laptop, however, I saw no confirmation the patch had been installed.
  • Normally, Windows 10 users can use the Windows Update tab within the Update and Security settings section to ensure patches have been installed.
  • Attackers can exploit the flaws to execute code of their choice or to obtain information stored on vulnerable systems.
  • Tuesday’s advisories didn’t say if exploits worked only when targets opened the malformed images in specific apps or any app.

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AI Feynman 2.0: Learning Regression Equations From Data

  • Symbolic regression is a way of stringing together the user-specified mathematical functions to build an equation for the output “y” that best fits the provided dataset.
  • We can see from running the code snippet above that the target “y” data points in example1.txt are generated using the equation on line 3, where the inputs are all the columns except for the last one, and the equation generates the last column.
  • The solver runs for a long time, trying different kinds of equations at different levels of complexity, and assessing the best fit for each one.
  • Now that we took a peek at what our data looks like, let’s ask the solver to find a simple equation that fits our data, using our dataset.
  • The idea is that we want the solver to notice that you don’t need all of the supplied variables in order to fit the data.

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Dynamic Linking

  • Instead a program called the dynamic linker will be responsible for taking code from the .so files and adding them into an executing program.
  • When we link with a dynamic, shared object, those symbols that can be added at run time will remain unresolved.
  • These symbols have stubs that will eventually invoke the interpreter to load the actual code for that function.
  • At first, the address in the GOT is the code that loads the program (the dynamic linker).
  • In our modified program, we look at 0x12018, which was where our some_func stub was located in the global offset table (remember [email protected] referred us to this location in the GOT).
  • Notice that at first the global offset table points to 0x104e0, which is the code used to invoke the interpreter which in turn loads the symbols.

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AI Feynman 2.0: Learning Regression Equations From Data

  • Symbolic regression is a way of stringing together the user-specified mathematical functions to build an equation for the output “y” that best fits the provided dataset.
  • We can see from running the code snippet above that the target “y” data points in example1.txt are generated using the equation on line 3, where the inputs are all the columns except for the last one, and the equation generates the last column.
  • The solver runs for a long time, trying different kinds of equations at different levels of complexity, and assessing the best fit for each one.
  • Now that we took a peek at what our data looks like, let’s ask the solver to find a simple equation that fits our data, using our dataset.
  • The idea is that we want the solver to notice that you don’t need all of the supplied variables in order to fit the data.

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A Deep Dive into V8

  • While the compiler, in fact, generates a whole program at the end of the process, the interpreter works as a program itself that does the job by reading the instructions (usually as scripts, like the JavaScript scripts) and translating them into executable commands.
  • This is not possible with languages like Java, for example, in which everything (classes, method, objects and variables) must be defined before program execution and can’t be dynamically changed after the app starts.
  • Because of its particular nature, the JavaScript interpreters usually perform a dictionary lookup based on a hash function to know exactly where this variable or that object is allocated in memory.
  • When you execute the first line of code in this function, a new hidden class is going to be created based on the previous one, this time _User1.

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The 4 Steps to Branching in Git that Data Scientists Should Know

  • All jokes aside, understanding how and when to branch is critical to working with Git. I’ll walk you through the four-step process I use on my projects.
  • If you work in a group, you may want to designate one person as the repo owner and designated them as the one person that can create a new feature branch.
  • The proper way to patch a tag would be to check out the branch it was derived from and create a new branch from there.
  • This means you would add the patch code to a new v0.1.1-features branch and then merge those changes back into master and tag it as v0.1.1.
  • If the master branch is ahead of the feature branch you are patching; you need to decide if you want to tag from the current branch or merge to master and include those changes in the next release’s tag.

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