PYTHON Study Guide - Numpy, Pycharm

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vimalmsr02

26 May 2023

School

Anna University - Chennai

Department

Graphic Design

Course

PYTHON

Professor

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Document Summary

Section overview: in this document, the speaker introduces multi- dimensional arrays in python and explains how they differ from single- dimensional arrays. The speaker also discusses the limitations of using the built-in array package for multi-dimensional arrays and introduces the numpy package as a solution. A single-dimensional array has one row and multiple columns. A multi-dimensional array has multiple rows and columns. Multi-dimensional arrays can be 2d, 3d, or even 4d. The built-in array package in python does not support multi-dimensional arrays. Attempting to create a multi-dimensional array with the built-in package will result in an error. Numpy is a third-party package that allows for working with multi- dimensional arrays in python. To use numpy, it must first be installed using pip3. Once installed, numpy can be imported into your code using . Section overview: in this section, the instructor explains how to install and use numpy in pycharm.

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Deep Learning With PyTorch - Full Course

Patrick Loeber

AllInOne Pie Torch Video This video is a combination of all the parts from the beginner pie torch playlist, providing a comprehensive course that covers the fundamentals of the deep learning framework. By the end of this course, you'll have the confidence to handle intermediate-level tasks. In this first video, I'll show you how to install PyTorch, so let's get started. The CUDA toolkit is a development environment that enables the creation of high-performance GPU-accelerated applications. To use it, you'll need an NVIDIA GPU in your machine. If you're on Linux or Windows and want to have GPU support, you'll need to install the CUDA toolkit, and the newest version is 10.2. In PyTorch, everything is based on tensor operations. Numpy arrays and vectors are familiar to most people, and in PyTorch, everything is a tensor. A tensor can have different dimensions. For instance, if we set the value to three, it's a one-dimensional vector with three elements. We can also make it two-dimensional. You'll also learn how to convert numpy arrays to PyTorch tensors and vice versa.

We can construct a tensor from data, such as a Python list: set = x + y performs element-wise addition: c = set We can also use subtraction: set = x - y To reshape a tensor or convert between Numpy and a Torch tensor: First, import Numpy: import numpy We must ensure the number of elements remains the same: For example, to reshape a 4x4 tensor so that the two dimensions are unspecified: new_tensor = torch.Tensor(numpy_array).reshape(-1, 2) When tensors are on the CPU (not on the GPU), they share the same memory location: This means that if we modify one, we also modify the other: a.add_(1) (The underscore functions modify in place.)

Thank you for watching this tutorial. If you found it helpful, please consider subscribing to our channel. We covered the autograd package in PyTorch and how it allows us to calculate gradients, which are essential for optimizing our models. This is an important concept to

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PYTHON Study Guide - Numpy, Pycharm

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