I can’t actually share all the code. So this feels incomplete. But I can share what I said about the code. Then you can look at your code and decide if you’ve got similar problems to fix.
My responses were these. I’ll expand on them below.
Articles
In this episode, you will learn the basics of Python decorators and what the are good for.
You can read the chapter this screencast is based on here: http://python101.pythonlibrary.org/ or purchase the book on Leanpub
What Is NumPy?
NumPy is a powerful Python library that is primarily used for performing computations on multidimensional arrays. The word NumPy has been derived from two words — Numerical Python. NumPy provides a large set of library functions and operations that help programmers in easily performing numerical computations. These kinds of numerical computations are widely used in tasks like:
- Machine Learning Models: while writing Machine Learning algorithms, one is supposed to perform various numerical computations on matrices. For instance, matrix multiplication, transposition, addition, etc. NumPy provides an excellent library for easy (in terms of writing code) and fast (in terms of speed) computations. NumPy arrays are used to store both the training data as well as the parameters of the Machine Learning models.
- Image Processing and Computer Graphics: Images in the computer are represented as multidimensional arrays of numbers. NumPy becomes the most natural choice for the same. NumPy, in fact, provides some excellent library functions for fast manipulation of images. Some examples are mirroring an image, rotating an image by a certain angle, etc.
- Mathematical tasks: NumPy is quite useful to perform various mathematical tasks like numerical integration, differentiation, interpolation, extrapolation, and many others. As such, it forms a quick Python-based replacement of MATLAB when it comes to Mathematical tasks.
NumPy Installation
The fastest and the easiest way to install NumPy on your machine is to use the following command on the shell: pip install numpy.
Python and Machine Learning
In this article, we will introduce you to Machine Learning with Python. Moreover, we will discuss Python Machine Learning tasks, steps, and applications. Then, we will take a look at 10 tech giants that adopt Python Machine Learning to improve what they do.
So, let’s begin!
At Grakn, we recently released Grakn 1.3, with a slew of new features, bug fixes, and performance enhancements. Included in this release are new gRPC-based drivers for Java, NodeJS, and Python. This article will walk you through the Python driver and provide guidelines on how you can write your own for your language of choice.
Overview
The main reason for rewriting our drivers was a move from REST to gRPC in Grakn. This change has cleaned up our API and should provide performance benefits. Further, all of our available drivers (Java, Node, and Python) now expose the same objects and methods to users, subject to language naming conventions and available types. To maintain this uniformity across the stack, new language drivers should provide the same interface. Note that you will require both gRPC and protobuf support to create a functioning driver, so double check a) that compilers for your language exist, and b) your target language version is compatible with the compiler.
Data science is all about capturing data in an insightful way, whereas Machine Learning is a key area of it. Data science is a fantastic blend of advanced statistics, problem-solving, mathematics expertise, data inference, business acumen, algorithm development, and real-world programming ability. And Machine Learning is a set of algorithms that enable software applications to become more precise in predicting outcomes or take actions to separate it without being explicitly programmed.
The distinction between data science and Machine Learning is a bit fluid, but the main idea is that data science emphasizes statistical inference and interpretability, while Machine Learning prioritizes predictive accuracy over model interpretability. And for both data science and Machine Learning, open source has become almost the de facto license for innovative new tools.
In this post we will talk about creating Python Lists of Tuples and how they can be used.
Python Lists
Lists in Python are simply an array. Here is a basic list of my favorite WoW Classes:
