![]() Elements are read from the original array in a certain index order and written to a new array in the same index order. This is a rather simple example, but you can also use it for reshaping from and to arrays with more dimensions. ![]() ![]() The argument to reshape is the new shape, a tuple of the desired dimensions. Or you might use NumPy as the result of a library function call.Ī NumPy array, in as many dimensions as you want, can be directly created from nested lists, nested tuples, or a combination of those, as long as the dimensions make sense. Therefore, you’ll often use NumPy directly when you have a dataset in one specific format and you have to transform it into another format. A NumPy array is a very common input value in functions of machine learning libraries. If you went through the previous modules, everything you need is already installed! NumPyĪs noted in Module 4, the core of NumPy is its N-dimensional arrays, and it also offers features such as linear algebra and Fourier transforms. In this module, I'll also give a short overview of the scikit-learn library, because it's the most complete machine learning (excluding deep learning) library in the Python ecosystem. Nevertheless, it's a good idea to take a look at the lower-level libraries to see what they're about. That would be more for data scientists, dedicated AI/ML engineers, and developers of higher-level ML libraries. If you’re an enterprise developer, you won't be writing complete solutions with just these libraries (it takes much longer and is harder to maintain). ![]() Because they’re the building blocks of machine learning libraries, you'll definitely come across them at some point. Now we’re going to take a quick look at NumPy and TensorFlow. In the previous one, we discussed neural networks with Keras. This is the eighth and last module in our series on Python and its use in machine learning and AI. ![]()
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