env \ PATH= »$(brew –prefix tcl-tk)/bin:$PATH » \ LDFLAGS= »-L$(brew –prefix tcl-tk)/lib » \ CPPFLAGS= »-I$(brew –prefix tcl-tk)/include » \ PKG_CONFIG_PATH= »$(brew –prefix tcl-tk)/lib/pkgconfig » \ CFLAGS= »-I$(brew –prefix tcl-tk)/include » \ PYTHON_CONFIGURE_OPTS= »–with-tcltk-includes=’-I$(brew –prefix tcl-tk)/include’ –with-tcltk-libs=’-L$(brew –prefix tcl-tk)/lib -ltcl8.6 -ltk8.6′ » \ pyenv install
In this tutorial, you’ll learn everything you need to know to get up and running with NumPy, Python’s de facto standard for multidimensional data arrays. NumPy is the foundation for most data science in Python, so if you’re interested in that field, then this is a great place to start.
Discussions:Hacker News (366 points, 21 comments), Reddit r/MachineLearning (256 points, 18 comments)Translations: Chinese 1, Chinese 2, Japanese The NumPy package is the workhorse of data analysis, machine learning, and scientific computing in the python ecosystem. It vastly simplifies manipulating and crunching vectors and matrices. Some of python’s leading package rely on NumPy as a fundamental piece of their infrastructure (examples include scikit-learn, SciPy, pandas, and tensorflow). Beyond the ability to slice and dice numeric data, mastering numpy will give you an edge when dealing and debugging with advanced usecases in these libraries.In this post, we’ll look at some of the main ways to use NumPy and how it can represent different types of data (tables, images, text…etc) before we can serve them to machine learning models.
I don’t know about you but in these times of working from home and social distancing I get way more annoyed when internet speeds are not reaching the levels required for a decent video conference. I have three routers in my home all of which are running FreshTomato firmware. In