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Free Cash Flow (or, FCF) is a term widely used in Corporate Finance. It is the extra cash flow available with the company after deducting the working capital expenditures and the expenses towards fixed assets. It is significant because it shows the flexibility of a company to meet newer expenditures or to allow more payment to security holders and creditors without affecting the operations of the company. FCFs are of interest to the creditors, lenders, borrowers and financial managers, as it is related to the financial wellbeing of a company.How to Calculate Free Cash Flows?FCF can be calculated in a ... Read More
Financial FlexibilityFinancial flexibility refers to the capability of a company to respond to its cash flow or an investment opportunity set in a timely and value-maximizing manner. The concept of financial flexibility is not new, but the most corporate approach has been via the Miller and Modigliani model of capital structure where the corporate performance is judged in a perfect capital market. Such capital markets are frictionless, and they perform in a costless manner where the firms can enjoy complete flexibility of arranging their capital structure.Financial Flexibility is Important in Uncertain TimesFinancial flexibility plays a critical role only when the ... Read More
Free Cash Flow (or FCF) is a widely used metric in finance and it is sometimes known as the unlevered cash flow. But before we dive deeper into why FCFs are called so, let's begin with what FCFs are.What is Free Cash Flow?Free Cash Flow is a cash component that a company retains after investing and distributing money to all kinds of debt outstanding in the market. FCF is a measure of the wellbeing of a company and so, it is of interest to the lenders and debt-holders of the company.Simply put, FCF is the funds that remain after repaying ... Read More
In Free Cash Flow (FCF) method, the interest tax shield is adjusted in the discount rate which is also called weighted average cost of capital (WACC). The adjustment is not done in cash flows of the firm. We can take an alternate measure to adjust the cash flow where the adjustments are not made in tax shields of the business. This is known as Capital Cash Flow (CCF) approach.In this approach, interest tax shields are adjusted in cash flows rather than in discount rates. This method of adjusting tax shields in cash flow is known as capital cash flow. In ... Read More
A temporal data can be represented as a 1D tensor, and spatial data as 2D tensor while a volumetric data can be represented as a 3D tensor. The Upsample class provided by torch.nn module supports these types of data to be upsampled. But these data must be in the form N ☓ C ☓ D (optional) ☓ H (optional) ☓ W (optional), Where N is the minibatch size, C is the numberchannels, D, H and W are depth, height and width of the data, respectively. Hence, to upsample a temporal data (1D), we need it to be in 3D in ... Read More
The saturation of an image refers to the intensity of a color. The higher the saturation of a color, the more vivid it is. The lower the saturation of a color, the closer it is to gray.To adjust the saturation of an image, we apply adjust_saturation(). It's one of the functional transforms provided by the torchvision.transforms module. adjust_saturation() transformation accepts both PIL and tensor images. A tensor image is a PyTorch tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width.This transform also accepts a batch ... Read More
We can apply a linear transformation to the input data using the torch.nn.Linear() module. It supports input data of type TensorFloat32. This is applied as a layer in the deep neural networks to perform linear transformation. The linear transform used −y = x * W ^ T + bHere x is the input data, y is the output data after linear transform. W is the weight matrix and b is biases. The weights W have shape (out_features, in_features) and biases b have shape (out_features). They are initialized randomly and updated during the training of a Neural Network.Syntaxtorch.nn.Linear(in_features, out_features)Parametersin_features - It ... Read More
A tensor can be flattened into a one-dimensional tensor by reshaping it using the method torch.flatten(). This method supports both real and complex-valued input tensors. It takes a torch tensor as its input and returns a torch tensor flattened into one dimension.It takes two optional parameters, start_dim and end_dim. If these parameters are passed, only those dimensions starting with start_dim and ending with end_dim are flattened.The order of elements in the input tensor is not changed. This function may return the original object, a view, or copy. In the following examples, we cover all the aspects of flattening the tensor ... Read More
To compute the cross entropy loss between the input and target (predicted and actual) values, we apply the function CrossEntropyLoss(). It is accessed from the torch.nn module. It creates a criterion that measures the cross entropy loss. It is a type of loss function provided by the torch.nn module.The loss functions are used to optimize a deep neural network by minimizing the loss. CrossEntropyLoss() is very useful in training multiclass classification problems. The input is expected to contain unnormalized scores for each class.The target tensor may contain class indices in the range of [0, C-1] where C is the number ... Read More
Mean squared error is computed as the mean of the squared differences between the input and target (predicted and actual) values. To compute the mean squared error in PyTorch, we apply the MSELoss() function provided by the torch.nn module. It creates a criterion that measures the mean squared error. It is also known as the squared L2 norm.Both the actual and predicted values are torch tensors having the same number of elements. Both tensors may have any number of dimensions. This function returns a tensor of a scalar value. It is a type of loss function provided by the torch.nn ... Read More