Category: Python

Feed: R-bloggers. Author: DataScienceTutor. [This article was first published on Data Science Tutorials, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. The post Best Books to Learn R Programming appeared first onData Science Tutorials – Best Books to Learn R Programming, R and Python are now the most popular programming languages for performing data science, and each has its own set of advantages and downsides. Python is preferred for Machine Learning because of ... Read More

Feed: R-bloggers. Author: sang-heon lee. #========================================================## Quantitative ALM, Financial Econometrics & Derivatives # ML/DL using R, Python, Tensorflow by Sang-Heon Lee ## https://kiandlee.blogspot.com#——————————————————–## IRRBB Interest Rate Shock Scenarios#========================================================#graphics.off(); rm(list = ls())#=======================================================# read data#=======================================================str.zero – “mat rate     0.083333333    0.01529     0.25            0.01648     0.5            0.01815     0.75            0.01947     1              0.02045     2              0.02252     3              0.02410     4              0.02549     5              0.02637     6              0.02730     7              0.02787     8              0.02825     9              0.02857     10              0.02881     12              0.02913     15              0.02953     20              0.03042″df – read.table(text = str.zero, header = TRUE)#=======================================================# make shocks#=======================================================# size of shocks by each currency# USD parameter : BCBS (2016)r_bar_p_c – 0.02r_bar_s_c – 0.03r_bar_l_c – 0.015# placeholder for rate shocksdr – data.frame(base = rep(0,length(df$mat)))# Parallel shock up/downdr$pu –  r_bar_p_cdr$pd – –r_bar_p_c# Short rate shock up/downdr$su –  r_bar_s_c*exp(–df$mat/4)dr$sd – –r_bar_s_c*exp(–df$mat/4)# Long rate shockdrlong – r_bar_l_c*(1–exp(–df$mat/4))# Rotational shock dr$steep – –0.65*dr$su + 0.9*drlongdr$flat  –   0.8*dr$su – 0.6*drlongdr#=======================================================# generate shocked curves#=======================================================(df.irrbb – 0.01 + dr)x11(width = 5, height = 4) matplot(df$mat, df.irrbb, type=“l”, lty = 1, lwd = 3,        xlab = “Maturity(year)”, ylab = “Yield(decimal)”,        main = “IRRBB IR shock scenarios (Base = 1%)”)(df.irrbb – df$rate + dr)x11(width = 5, height = 4) matplot(df$mat, df.irrbb, type=“l”, lty = 1, lwd = 3,        xlab = “Maturity(year)”, ylab = “Yield(decimal)”,        main = “IRRBB IR shock scenarios”) ... Read More

Feed: Planet PostgreSQL. In the past few months, there’s been a single recurring question that appears with alarming regularity in the Postgres Discord server. The phrasing itself ranges anywhere from “Hi, I’ve recently installed Postgres, but I don’t know how to connect to it?” to “I keep getting errors when I try to connect to Postgres!” How does this scenario erupt so frequently? Is it something about Postgres? New users? People not familiar with databases in general?How this Happens New Postgres users are usually pretty easy to spot. They’re the ones most likely to express confusion or incredulity that there ... Read More

Feed: SAS Blogs. Author: Jagruti Kanjia. Authors: Ricky Tharrington and Jagruti Kanjia In this post, you’ll learn how to assess the bias of machine learning models using tools available in SAS Viya 4. As artificial intelligence grows in popularity, the lives of individuals are increasingly impacted by the predictions of machine learning models in ways ranging from ad suggestions on social media to credit application approvals. Regardless of the importance of the scenario, every model application has the potential to affect separate segments of the user population differently. It’s important for data scientists to understand the biases present in their ... Read More

Feed: Domo Blog. Author: Ben Schein. It is springtime in the U.S., which means something as American as apple pie is back: baseball. And since there’s all kinds of great data around one of the country’s great pastimes, we decided for this week’s post to look at Major League Baseball (MLB) attendance statistics from the last 20 years, which is published on many websites including the one we used to get the data you’ll find in the charts below: ESPN.com.To collect the attendance data from ESPN, we used Jupyter Workspaces (currently in beta in Domo) and the Python package Beautiful Soup to parse the HTML. And since Domo ... Read More

Feed: AWS Compute Blog. Author: James Beswick. This post is written by Dan Fox, Principal Specialist Solutions Architect, Serverless. You can now develop AWS Lambda functions using the Node.js 16 runtime. This version is in active LTS status and considered ready for general use. To use this new version, specify a runtime parameter value of nodejs16.x when creating or updating functions or by using the appropriate container base image. The Node.js 16 runtime includes support for ES modules and top-level await that was added to the Node.js 14 runtime in January 2022. This is especially useful when used with Provisioned Concurrency to reduce cold start ... Read More

Feed: The Internet of Things on AWS – Official Blog. Author: Syed Rehan. In a connected device, you may need to monitor devices in near-real time to detect error and mitigate actions, Last Will and Testament (LWT) method for MQTT addresses this challenge. LWT is a standard method of MQTT protocol specification that allows to detect abrupt disconnects of devices and to notify other clients about this abrupt disconnections. IoT devices are often used in environments with unreliable network connectivity and/or devices might disconnect due to lack of power supply, low battery, loss of connection, or any other reason. This ... Read More

Feed: Radar. Author: Mike Loukides. Several weeks ago, I had a great conversation with Sebastian Hassinger about the state of quantum computing. It’s exciting–but also, not what a lot of people are expecting. I’ve seen articles in the trade press telling people to invest in quantum computing now or they’ll be hopelessly behind. That’s silly. There are too many people in the world who think that a quantum computer is just a fast mainframe. It isn’t; quantum programming is completely different, and right now, the number of algorithms we know that will work on quantum computers is very small. You ... Read More

Feed: AWS Partner Network (APN) Blog. Author: Steve Steuart. By Steve Steuart, WW Principal Mainframe Modernization GTMS – AWSBy Skyla Loomis, Vice President, IBM zSystems Application Platform – IBM IBM To meet the needs of a digitally transformed business, customers are modernizing mainframe applications to increase agility, maximize the value of their investments, and innovate faster. At re:Invent 2021, Amazon Web Services (AWS) announced AWS Mainframe Modernization to help customers modernize their mainframe workloads with a managed and highly available runtime environment on AWS. On the path to modernization, we are also seeing customers choose a hybrid cloud strategy that ... Read More

Feed: R-bloggers. Author: %>% dreams. [This article was first published on %>% dreams, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. As I’ve been saying every year for the past seven years or so, I am learning Python. (It’s been a journey.) Python packages like pandas have several ways to work with data. There are several options for indexing, slicing, etc. They have a lot of flexibility but also a lot of conventions ... Read More