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Reductions (or aggregations) are a family of NumPy functions that operate over an array returning a result with fewer dimensions. Many of these functions perform typical statistical operations on arrays, while others perform dimensionality-reductions. In this article, we will learn about some of the most common aggregations, but before we get ...

Universal functions (ufunc) are special NumPy functions that operate on ndarrays in an element-by-element fashion. They represent a vast array of vectorized functions that perform much better than iterative implementations and let you write concise code. Most ufuncs achieve this by providing a Python wrapper around a C implementation. In this ...

NumPy array indexing is a big topic, and there are many different ways of selecting elements from an array. Let’s start with the simplest case: selecting an entry from a 1-dimensional array. import numpy as np arr = np.arange(10) print(arr) [0 1 2 3 4 5 6 7 8 9] You can access elements from a 1-dimensional array in NumPy using the same sy...

In the last article, we learned many different ways in which we can create ndarrays. Now that we know how to create NumPy arrays it’s time to start playing around with them. We will learn to perform basic operations, a task you can divide into 3 categories: Operations between arrays of the same size. Operations between an array and a scal...

NumPy (an acronym for Numeric Python) is a library for working with multi-dimensional arrays and matrices. It was created in 2005 by Travis Oliphant, and since then received numerous contributions from the community that enabled it to grow into one of the most used tools in data science. NumPy lets you manipulate huge arrays in a very performan...

We reached the end of our introductory journey in deep learning. Now you understand what this is all about. Maybe you really like it and are ready to deepen your knowledge in the topic(deepen, deep learning, get it? Uhgg). This will be a shorter article, I’ll just offer some pointers you can follow as next steps. Good, let’s get started! ...

In the previous article we learned how to use Keras to build more powerful neural networks. Professional-grade libraries like Keras, Tensorflow, and Pytorch let you build neural networks that can learn intricate patterns and solve novel problems. Deep-learning networks lets learn subtle patterns thanks to their inherently large hypothesis space...

We already covered the most important deep learning concepts and created different implementations using vanilla Python. Now, we are in a position where we can start building something a bit more elaborate. We’ll use a more hands-on approach by building a deep learning model for classification using production-grade software. You will learn ho...