Automated batch refactoring has become a de-facto mechanism to restructure software that may have significant design flaws negatively impacting the code quality and maintainability. Although automated batch refactoring techniques are known to significantly improve overall software quality and maintainability, their impact on resource utilization is not well studied. This paper aims to bridge the gap between batch refactoring code smells and consumption of resources. It determines the relationship between software code smell batch refactoring, and resource consumption. Next, it aims to design algorithms to predict the impact of code smell refactoring on resource consumption. This paper investigates 16 code smell types and their joint effect on resource utilization for 31 open source applications. It provides a detailed empirical analysis of the change in application CPU and memory utilization after refactoring specific code smells in isolation and in batches. This analysis is then used to train regression algorithms to predict the impact of batch refactoring on CPU and memory utilization before making any refactoring decisions. Experimental results also show that our ANN-based regression model provides highly accurate predictions for the impact of batch refactoring on resource consumption. It allows the software developers to intelligently decide which code smells they should refactor jointly to achieve high code quality and maintainability without increasing the application resource utilization. This paper responds to the important and urgent need of software engineers across a broad range of software applications, who are looking to refactor code smells and at the same time improve resource consumption. Finally, it brings forward the concept of resource aware code smell refactoring to the most crucial software applications.

Become an expert and get hired. Welcome to the best resource for learning OpenAI API with Python and for integrating the latest OpenAI models into your applications. This OpenAI API with Python Bootcamp covers every model released by OpenAI that has an API, including GPT-3 (Davinci), ChatGPT (gpt-3.5-turbo), By the end of this course, you'll have in-depth knowledge and a vast hands-on experience with the OpenAI API and you'll be an expert able to make your Python applications intelligent. This is a brand new OpenAI API course that will be constantly updated (with GPT-4 included) to teach you the skills required for the future that comes.

My name is Filip Projcheski, I am 23 years old and I am a Computer Science Engineer and a Machine Learning/Data Science enthusiast. I have skills in a couple of programming languages including Python, C#, Java, R, C/C and JavaScript. I work as a Software Engineer in a new startup where we work on very interesting projects like: making costumes for VR games, making Instagram bots that will make you an influencer, as well as many CRUD web applications. My favorite AI fields are: Reinforcement Learning, Computer Vision and Time-Series Analyses.

Python frameworks help project owners fast-track their application's time-to-market. In this entry, let's answer the pressing need of startups to understand the difference between Python's frameworks and libraries. What are frameworks and libraries?" As a project owner(startup), this may be the question you ask when developers seek your thoughts on the best python frameworks or libraries to use. Now, before you panic and call a lifeline, think first.

We are pleased to announce the launch of Python support for Amazon CodeGuru, a service for automated code reviews and application performance recommendations. CodeGuru is powered by program analysis and machine learning, and trained on best practices and hard-learned lessons across millions of code reviews and thousands of applications profiled on open-source projects and internally at Amazon. The launch of Python support extends CodeGuru beyond its original Java support. Python is a widely used language for various use cases, including web app development and DevOps. Python's growth in data analysis and machine learning areas is driven by its rich frameworks and libraries.

If you are thinking to learn a new programming language then also Python is a good choice, particularly if you are looking to move towards a lucrative career path of Data Science and Machine learning which has lots of opportunities. In this article, I am going to share some of the best online courses to learn Python in 2020... Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options. Python is relatively simple, so it's easy to learn since it requires a unique syntax that focuses on readability. Developers can read and translate Python code much easier than other languages.

Let's assume we have a pump that we want to monitor to detect abnormal pump behavior. In SAP Predictive Maintenance and Service, the pump's operation mode and its rotational speed are recorded. To this end, a Gaussian kernel density estimator will be used [3]. This is a very simple use case, that could also be achieved in many other ways. While the use case presented here is very simple, the coding developed here can be used as template for other use cases as well to use different algorithms with SAP Predictive Maintenance.

One of the big surprises of the past few years has been the spectacular rise in the use of Python* in high-performance computing applications. With the latest releases of Intel Distribution for Python, included in Intel Parallel Studio XE 2019, the numerical and scientific computing capabilities of high-performance Python now extends to machine learning and data analytics. Because it's easy to learn and comes with vast open source packages and libraries tailored for just about every computation domain, especially data analytics and machine learning. Industrial strength data analytics involves some very serious math. A single application might employ many complex solutions requiring a significant effort to develop.

As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below. If any of this interests you, check out the jobs site or find us at PyCon. We have donated a few Netflix Originals posters to the PyLadies Auction and look forward to seeing you all there.

IBM has been tweaking the AI-powered highlight picking algorithm it deploys during the Wimbledon tennis championships this year to take into account a wider array of factors to better find and personalise the best points to share with fans around the world. Big Blue is celebrating a 30-year technology partnership with the famous grass court tennis tournament, and in 2017 it unveiled an AI-powered system for picking the best points to insert into a highlights package, with the aim of delivering highlights "better than an international media organisation" as Sam Sneddon, IBM sports and entertainment lead, told Computerworld UK during a tour of its technology bunker on-site at the Championships this year. Whether it was Novak Djokovic and Roger Federer's five-hour epic mens' final, or Simona Halep's swift dismantling of Serena Williams in the ladies' final, IBM was working in the background to map and collect every second of footage before feeding it through a set of machine learning and deep learning algorithms which decide the points that would make for the best 5-10 minute highlight package. The Watson system analyses 39 factors, like player gestures and crowd reactions, from live footage and assigns an'excitement score'. For an idea of scale, IBM collects 4.5 million tennis data points per tournament.