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Starting out new on Canva and completely overwhelmed? When first starting out, Canva can be a tricky place to find your way through. The wide assortment of features which makes designing very easy may also sometimes lead to confusion if you are not well-versed with the platform. This may keep users from discovering the multiple smart and underrated uses of these features.Smart and unique ways to use Canva for Social MediaIn this article, we will see how you can use Canva and its features to promote a brand in smart and unique ways on Social Media platforms.Branding ImagesIf the images ... Read More
Want to explore your creativity as a graphic designer? Tired of using the same predefined templates? This is the right place for you. Canva provides you with the opportunity to custom create your template designs so that you can create exactly what you need with all the components of your choice.What are templates in Canva?Canva is a free graphics design platform which aids in creating a wide range of visual content such as presentation, invitations, posters, business cards and various other for use in social media and other visual platforms. Templates are the building blocks of these designs they are ... Read More
The infinite series of sine and cosine terms of frequencies $0, \omega_{0}, 2\omega_{0}, 3\omega_{0}, ....k\omega_{0}$is known as trigonometric Fourier series and can written as, $$\mathrm{x(t)=a_{0}+\sum_{n=1}^{\infty}a_{n}\:cos\:n\omega_{0} t+b_{n}\:sin\:n\omega_{0} t… (1)}$$Here, the constant $a_{0}, a_{n}$ and $b_{n}$ are called trigonometric Fourier series coefficients.Evaluation of a0To evaluate the coefficient $a_{0}$, we shall integrate the equation (1) on both sides over one period, i.e., $$\mathrm{\int_{t_{0}}^{(t_{0}+T)}x(t)\:dt=a_{0}\int_{t_{0}}^{(t_{0}+T)}dt+\int_{t_{0}}^{(t_{0}+T)}\left(\sum_{n=1}^{\infty}a_{n}\:cos\:n\omega_{0} t+b_{n}\:sin\:n\omega_{0} t\right)dt}$$$$\mathrm{\Rightarrow\:\int_{t_{0}}^{(t_{0}+T)}x(t)\:dt=a_{0}T+\sum_{n=1}^{\infty}a_{n}\int_{t_{0}}^{(t_{0}+T)}cos\:n\omega_{0} t\:dt+\sum_{n=1}^{\infty}b_{n}\int_{t_{0}}^{(t_{0}+T)}sin\:n\omega_{0} t\:dt… (2)}$$As we know that the net areas of sinusoids over complete periods are zero for any non-zero integer n and any time $t_{0}$. Therefore, $$\mathrm{\int_{t_{0}}^{(t_{0}+T)}cos\:n\omega_{0} t\:dt=0\:\:and\:\:\int_{t_{0}}^{(t_{0}+T)}sin\:n\omega_{0} t\:dt=0}$$Hence, from equation (2), we get, $$\mathrm{\int_{t_{0}}^{(t_{0}+T)}x(t)\:dt=a_{0}T}$$$$\mathrm{\therefore\:a_{0}=\frac{1}{T}\int_{t_{0}}^{(t_{0}+T)}x(t)\:dt… (3)}$$Using equation (3), ... Read More
What is Fourier Series?In the domain of engineering, most of the phenomena are periodic in nature such as the alternating current and voltage. These periodic functions could be analysed by resolving into their constituent components by a process called the Fourier series.Therefore, the Fourier series can be defined as under −“The representation of periodic signals over a certain interval of time in terms of linear combination of orthogonal functions (i.e., sine and cosine functions) is known as Fourier series.”The Fourier series is applicable only to the periodic signals i.e. the signals which repeat itself periodically over an interval from $(-\infty\:to\:\infty)$and ... Read More
To check if an object is a tensor or not, we can use the torch.is_tensor() method. It returns True if the input is a tensor; False otherwise.Syntaxtorch.is_tensor(input)Parametersinput – The object to be checked, if it is a tensor or not .OutputIt returns True if the input is a tensor; else False.StepsImport the required library. The required library is torch.Define a tensor or other object.Check if the created object is a tensor or not using torch.is_tensor(input).Display the result.Example 1# import the required library import torch # create an object x x = torch.rand(4) print(x) # check if the above ... Read More
The use of "with torch.no_grad()" is like a loop where every tensor inside the loop will have requires_grad set to False. It means any tensor with gradient currently attached with the current computational graph is now detached from the current graph. We no longer be able to compute the gradients with respect to this tensor.A tensor is detached from the current graph until it is within the loop. As soon as it is out of the loop, it is again attached to the current graph if the tensor was defined with gradient.Let's take a couple of examples for a better ... Read More
The backward() method is used to compute the gradient during the backward pass in a neural network.The gradients are computed when this method is executed.These gradients are stored in the respective variables.The gradients are computed with respect to these variables, and the gradients are accessed using .grad.If we do not call the backward() method for computing the gradient, the gradients are not computed.And, if we access the gradients using .grad, the result is None.Let's have a couple of examples to demonstrate how it works.Example 1In this example, we attempt to access the gradients without calling the backward() method. We notice ... Read More
A contiguous tensor is a tensor whose elements are stored in a contiguous order without leaving any empty space between them. A tensor created originally is always a contiguous tensor. A tensor can be viewed with different dimensions in contiguous manner.A transpose of a tensor creates a view of the original tensor which follows non-contiguous order. The transpose of a tensor is non-contiguous.SyntaxTensor.is_contiguous()It returns True if the Tensor is contiguous; False otherwise.Let's take a couple of example to demonstrate how to use this function to check if a tensor is contiguous or non-contiguous.Example 1# import torch library import torch ... Read More
To transpose a tensor, we need two dimensions to be transposed. If a tensor is 0-D or 1-D tensor, the transpose of the tensor is same as is. For a 2-D tensor, the transpose is computed using the two dimensions 0 and 1 as transpose(input, 0, 1).SyntaxTo find the transpose of a scalar, a vector or a matrix, we can apply the first syntax defined below.And for any dimensional tensor, we can apply the second syntax.For
The rank of a matrix can be obtained using torch.linalg.matrix_rank(). It takes a matrix or a batch of matrices as the input and returns a tensor with rank value(s) of the matrices. torch.linalg module provides us many linear algebra operations.Syntaxtorch.linalg.matrix_rank(input)where input is the 2D tensor/matrix or batch of matrices.StepsWe could use the following steps to get the rank of a matrix or batch of matrices −Import the torch library. Make sure you have it already installed.import torch Create a 2D tensor/matrix or a batch of matrices and print it.t = torch.tensor([[1., 2., 3.], [4., 5., 6.]]) print("Tensor:", t)Compute the rank ... Read More