This the second part of Reinforcement Learning (Q-learning). If you would like to understand the RL, Q-learning, and key terms please read Part 1.
In this part, we will implement a simple example of Q learning using the R programming language from scratch. It is expected from you to understand the basics of R programming and complete the reading of Part 1 of this article.
We are coding the algorithms using the R base package…Continue
Added by Nitin Agarwal on June 4, 2020 at 8:23pm — No Comments
Have you heard about AI learning to play computer games on their own and giving tough competitions to expert Human gamers?
A very popular example being Deepmind whose AlphaGo program defeated the South Korean Go world champion in 2016. Other than this there are other AI agents developed with the intent of playing Atari games like…Continue
Added by Nitin Agarwal on June 4, 2020 at 7:51pm — No Comments
Despite their advantages, Dynamic Shiny Modules can destabilize the Shiny environment and cause its reactive graph to be rendered multiple times. In this blog post, I present how to remove deleted module leftovers and make sure that your Shiny graph observers are rendered just once.
While working with advanced Shiny applications, you have most likely encountered the need for using Shiny…Continue
Added by Krystian Igras on May 11, 2020 at 12:30am — No Comments
For the last two years, RStudio has been organizing a competition to showcase the power and flexibility of Shiny as a framework for creating applications. Lately I’ve been devoting my career to making Shiny apps more beautiful, and…Continue
Added by Pedro Coutinho Silva on May 7, 2020 at 11:00pm — No Comments
Have you ever experience a situation where you want to import and combine hundred of datasets? If you do this manually, then it will take too much time. On the other hand, we can use a simple R programming to solve this problem in easily and quickly. Therefore, I would like to share two methods of combining all small files in a directory into a single dataset…
Added by satyajit maitra on April 30, 2019 at 6:00am — No Comments
In this tutorial, we shall take a journey together to explore the structure of the DrugBank database. We will observe how the drugs information is structured within DrugBank’s XML database and see how this information can be retrieved using R. Our main purpose here is parsing the database from its containing XML file. Let us…
Added by Mohammed Ali on January 14, 2019 at 9:31am — No Comments
Combining Pivot Billions with R to dive into whether the holiday spirit inspires bigger tips and which parts of New York experience this effect the most.…
Added by Benjamin Waxer on January 14, 2019 at 7:04am — No Comments
The Ultimate R Cheat Sheet simply put makes it easy to learn R. The Ultimate R Cheat Sheet saves you time by providing hyperlinks to the documentation and package-level cheat sheets for the most important packages in R. The latest update doubles its ultimateness by providing a second page that includes Special Topics:
Time Series, Forecasting, and Financial…
Added by Matt Dancho on January 7, 2019 at 3:00am — No Comments
The Ultimate R Cheatsheet
We are developing a revolutionary new system for teaching Business Analysis with R (Business Analysis with R is a new course we are developing at Business Science University). The system is revolutionary for a number of reasons (we’ll get to these in a minute). The cornerstone of our teaching process is the Data Science with R Workflow that was originally taught by Hadley Wickham and Garrett Grolemund in the excellent…Continue
Added by Matt Dancho on November 5, 2018 at 3:30am — No Comments
Matching and merging 2 files is task I find myself doing all of the time. Historically, I've used VLOOKUP in MS Excel and just worked around any limitations. Finally, I bit the bullet and wrote an R Function that does the trick faster, and with more flexibility.
Added by Ray Hall on October 18, 2018 at 9:30am — No Comments
Python and R are the two most commonly used languages for data science today. They are both fully open source products and completely free to use and modify as required under the GNU public license.
But which one is better? And, more importantly, which one should you learn?
Both are widely used and are standard tools in the hands of every data scientist.
The answer may surprise you – because as a professional data scientist, you should be ready to deal with…Continue
By Antonio Gulli, Amita Kapoor
Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x
In this book, you will learn how to efficiently use TensorFlow, Google's open…Continue
What is NLP?
Natural Language Processing (NLP) can be simply defined as teaching an algorithm to read and analyze human (natural) languages just like the human brain does, but a lot faster than a human could, more accurately and on very large amounts of data.
It is a great skill to have if you are an aspiring data scientist or data analyst because has…Continue
Added by Aymone Kouame on August 11, 2018 at 2:00pm — No Comments
Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services in SQLServer eliminates the need for data movement. Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database. Easily deploy your R/Python code with SQL stored procedures making them accessible in your…Continue
R Deep Learning Essentials
By Joshua F. Wiley
Get everything you need to know to enter the world of deep learning when it comes to R with this book. Get started from the packages you need to have for your side,…Continue
Added by Packt Publishing on May 15, 2018 at 10:00pm — No Comments
R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises.
Learn the fundamentals of data analysis in the second edition of Data Analysis with R, authored by data scientist…Continue
Added by Packt Publishing on May 8, 2018 at 10:30pm — No Comments
R language is a free statistical computing environment; hence there are multiple ways/packages to achieve a particular statistical/quantitative output. I am going to discuss here a concise list of R packages that one can use for the modeling of financial risks and/or portfolio optimization with utmost efficiency and effectiveness. The intended audience for this article is financial market analysts interested in using R, and also for quantitatively inclined folks…Continue
Added by Ranjit Mishra on April 28, 2018 at 2:30am — No Comments
Cluster.OBeu v1.2.1 release on CRAN
We are very pleased to announce Cluster.OBeu v1.2.1 on CRAN!Continue
Added by Kleanthis Koupidis on March 12, 2018 at 4:00am — No Comments
We will use a R package called rvest which was created by Hadley Wickham. This package simplifies the process of scraping web pages.…
Added by Deepanshu Bhalla on February 26, 2018 at 9:15am — No Comments