Starting from the algorithm to its implementation.
Optimization is a mathematical discipline that determines the “best” solution in a quantitatively well-defined sense. Mathematical optimization of the processes governed by partial differential equations has seen considerable progress in the past decade, and since then it has been applied to a wide variety of disciplines e.g., science, engineering, mathematics, economics, and even commerce. Optimization theory provides algorithms to solve well-structured optimization problems along with the analysis of those algorithms. A typical optimization problem includes an objective function that is to be minimized or maximized with the given constraints. Optimization theory provides algorithms…
Imagine you were asked to solve a complex problem, and to make things even more challenging you cannot use the tools you normally be using, which helps to solve the problem. Sounds challenging right? Without the strategy, it certainly would be. Here in this article, we will talk about the common process for solving complex problems, know as computational thinking.
Computational thinking is the thought process involved in formulating the problem and expressing its solution in such a way that computer, human, or machine can effectively carry it out.
Computational thinking involves 4 basic steps,
Unboxing the black box!
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks. CNN's have shown a remarkable state of the art performance in many applications such as in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, and natural language processing.
In this article, I will only focus on vectorizing the single convolution layer and not the whole convolution neural network.
In image processing, a kernel, convolution matrix, or mask is a small matrix. It is used for blurring, sharpening, embossing, edge detection, and more. …