In this tutorial, you’ll learn how to flatten an array with NumPy flatten function, meaning that an array is collapsed to a single dimension. The NumPy flatten function allows you to turn a multi-dimensional array into a single-dimensional array. The function allows you to easily flatten arrays in different ways, including column-wise and row-wise. By […]
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In this tutorial, you’ll learn how to use the NumPy stack() function to join NumPy arrays along various axes. NumPy is an essential Python library for anyone working with data in Python. The NumPy stack() function allows you to combine NumPy arrays in different ways, along NumPy axes. By the end of this tutorial, you’ll
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In this tutorial, you’ll learn how to use the NumPy squeeze() function. The np.squeeze() function allows you to remove single-dimensional entries from an array’s shape. This allows you to better transform arrays that aren’t shaped in the way that makes sense for the work that you’re doing. In short, the function returns the input array
NumPy Squeeze: Removing Dimensionality from NumPy Arrays Read More »
In this tutorial, you’ll learn how to use the NumPy repeat() function. The np.repeat() function is used to repeat arrays and provides significant opportunity to customize how the arrays are repeated. Being able to work with NumPy arrays is an important skill for data analysis, data science, and deep learning. By the end of this
NumPy Repeat Function: Repeating NumPy Arrays Read More »
In this tutorial, you’ll learn how to use the NumPy exponential function, np.exp(). The function raises the Euler’s constant, e, to a given power. Because Euler’s constant has many practical applications in science, math, and deep learning, being able to work with this function in meaningful ways is an asset for any Python user! By
NumPy Exponential: Using the NumPy.exp() Function Read More »
In this tutorial, you’ll learn how to implement the sigmoid activation function in Python. Because the sigmoid function is an activation function in neural networks, it’s important to understand how to implement it in Python. You’ll also learn some of the key attributes of the sigmoid function and why it’s such a useful function in
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In this tutorial, you’ll learn how to use the powerful NumPy pad function to pad arrays and matrices in NumPy and Python. The function is a powerful method that is often used in deep learning, especially in developing convolutional neural networks. Being able to master this function will allow you to build powerful deep learning
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In this tutorial, you’ll learn how to use the NumPy where() function to process or return elements based on a single condition or multiple conditions. The np.where() function is one of the most powerful functions available within NumPy. The function allows you to both return indices where a condition is met, or process array items
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In this tutorial, you’ll learn how to use the NumPy argmax() function to find the index of the largest value in an array. The np.argmax() function can be used to find the maximum value across an array, as well as across axes of different dimensions. The function can often be confusing, though this tutorial should
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In this tutorial, you’ll learn how to use the NumPy linspace function to create arrays of evenly spaced numbers. This can be incredibly helpful when you’re working with numerical applications. The NumPy linspace function allows you to create evenly spaced ranges of numbers and to customize these arrays using a wide assortment of parameters. By
NumPy linspace: Creating Evenly Spaced Arrays with np.linspace Read More »
In this tutorial, you’ll learn how to use the NumPy logspace function and how to use its different parameters. The np.logspace() function is used to return numbers that are evenly spaced on a log scale. The function allows you to specify many different attributes, including modifying the base of the log scale you want to
NumPy logspace: Understanding the np.logspace() Function Read More »
In this tutorial, you’ll learn how to use the NumPy tile function to arrange arrays using Python. The NumPy tile() function takes an array as an input and creates a new array by repeating the input array in different ways. Being able to work with and manipulate NumPy arrays is an important skill for data
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