According to Wikipedia, “Gradient descent is a first-order iterative optimization algorithm for finding local minima of a differentiable function“. Sounds a lot, right? In this article, let’s get acquainted with the Gradient descent algorithm in the most straightforward (and ‘simplest’) way. Before we continue with understanding the ABCDs of Gradient descent (and dig into the…
In this article, we’ll see a step by step forward pass (forward propagation) and backward pass (backpropagation) example. We’ll be taking a single hidden layer neural network and solving one complete cycle of forward propagation and backpropagation.
PyTorch is a deep learning framework that significantly simplifies the process of writing and training deep neural networks. It supports a wide range of architectures, from shallow ones to deep ones like transformers. I mean, any neural network architecture you can think of. On the other hand, tensors are fundamental data structures in PyTorch; they…
In this article, we’ll find the derivative of Sigmoid Function. The Sigmoid Function is one of the non-linear functions that is used as an activation function in neural networks.
This article is a beginner guide to norms from linear algebra. The commonly used norms: L1 norm, L2 norm, max-norm and Frobenius norm are also discussed.
In this article, I’ll discuss quickly how you can use twarc2 to access Twitter API v2 and search historical tweets.
In this article, we’ll see the basic operations (Addition, Broadcasting, Multiplication, Transpose, Inverse) that can be performed on tensors.
This article discusses the fundamentals of linear algebra: scalars, vector, matrices and tensors. The real-world use case of tensors is briefly introduced.
This article discusses in detail how you can extract complete Twitter data by hydrating tweet ids.