Book Image

Mastering Numerical Computing with NumPy

By : Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu
Book Image

Mastering Numerical Computing with NumPy

By: Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu

Overview of this book

NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts. Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system. By the end of this book, you will have become an expert in handling and performing complex data manipulations.
Table of Contents (11 chapters)

Computing gradient

When you have a linear line, you take the derivative so the derivative shows the slope of this line. Gradient is a generalization of the derivative when you have a multiple variable in your function, therefore the result of gradient is actually a vector function rather than a scalar value in derivative. The main goal of ML is actually finding the best model that fits your data. You can evaluate the meaning of the best as minimizing your loss function or objective function. Gradient is used for finding the value of the coefficients or a function that will minimize your loss or cost function. A well-known way of finding optimum points is taking the derivative of the objective function then setting it to zero to find your model coefficients. If you have more than one coefficient then it becomes a gradient rather than a derivative, and it becomes a vector equation...