Deep learning has improved the performance of neural network architectures such as recurrent neural networks (RNN and LSTM) and convolutional neural networks (CNN) in tackling a variety of Natural Language Processing (NLP) problems such as text categorisation, language modelling, machine translation, and so on. Transfer learning is a method of using a deep learning model that has been trained on a big dataset to perform similar tasks on a new dataset. A deep learning model like this is referred to as a pre-trained model. As a result, the demand for NLP transfer learning was at an all-time high. In the paper "Attention is All You Need," published in 2018, Google unveiled the transformer, which proved to be a watershed moment in NLP.

PyTorch Lightning is a Python package that provides a high-level interface for PyTorch, a well-known deep learning framework. It's a fast, lightweight framework that organises PyTorch code to separate research and engineering, making deep learning experiments easier to comprehend and reproduce.

Three cornerstones of quantitative finance are asset returns, interest rates, and volatilities. They appear in many fundamental formulas in finance. In this article, we consider their interplay and the underlying statistical issues in a classical topic in quantitative finance.

Jensen's inequality is perhaps the most famous theorem in quantitative finance (note that it is a "theorem" and not a model or a formula) and it is the reason why financial derivatives have value. Concept of convexity, Jensen's inequality, randomness and volatility of an asset price are intricately linked.

In empirical finance and many other domains, linear regression and the closely related linear prediction theory are commonly used statistical methods. Because of the wide range of applications, basic linear regression courses normally concentrate on the mathematically simplest scenario, which can be used in a variety of other applications.

Statistics is a discipline of applied mathematics that deals with the gathering, describing, analysing, and inferring conclusions from numerical data. Differential and integral calculus, linear algebra, and probability theory are all used substantially in statistics' mathematical theories.

The goal of this article is to create an algorithm that can understand simple spoken commands using the Speech Commands Dataset. We can improve product effectiveness and accessibility by improving the recognition accuracy of open-source voice interface tools.

On Ubuntu, I will show you how to instal RPM packages. The design of Debian-based systems like Ubuntu and RedHat-based systems like CentOS is quite similar. There are, however, a few minor differences. For example, RPM files are used to represent software packages on RedHat-based systems, while DEB files are used on Debian-based systems. An RPM package can be installed in one of two ways, by converting the RPM file to a DEB file or by installing the RPM file directly. Both approaches are easy, but depending on the package being installed, installing RPM packages on a Debian-based system can cause problems.

When adding statistics to the number of people and visits to the Hexo blog, according to the standard process of writing, pushing the number of people and visits after running found that the number of people simply do not load. Looking through the information found that the reason, busuanzi because in 2018/10/12 its domain name expired, so the number of people can not be displayed.

A supervised machine learning model has two phases, training and testing. When these models are learned, validated, and tested, the test and train data points are normally presumed to have the same distribution. In the real world, however, the training and test datasets rarely follow the same distribution.

This article tends to build a model that predicts stock price in the best way possible. This is an example of how you can use Long Short-Term Memory (LSTM) Neural Network on some real-world time series data with PyTorch. Hopefully, there are much better models that forecast the price of the stock.