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A radish in a tutu walking a dog? This AI can draw it really well

#artificialintelligence

An artist can draw a baby daikon radish wearing a tutu and walking a dog, even if they've never seen one before. But this kind of visual mashup has long been a trickier task for computers. Now, a new artificial-intelligence model can create such images with clarity -- and cuteness. This week nonprofit research company OpenAI released DALL-E, which can generate a slew of impressive-looking, often surrealistic images from written prompts such as "an armchair in the shape of an avocado" or "a painting of a capybara sitting in a field at sunrise." (And yes, the name DALL-E is a portmanteau referencing surrealist artist Salvador Dalí and animated sci-fi film "WALL-E.") A new AI model from OpenAI, DALL-E, can create pictures from the text prompt "an illustration of a baby daikon radish in a tutu walking a dog".


Fixed Priority Global Scheduling from a Deep Learning Perspective

arXiv.org Artificial Intelligence

Deep Learning has been recently recognized as one of the feasible solutions to effectively address combinatorial optimization problems, which are often considered important yet challenging in various research domains. In this work, we first present how to adopt Deep Learning for real-time task scheduling through our preliminary work upon fixed priority global scheduling (FPGS) problems. We then briefly discuss possible generalizations of Deep Learning adoption for several realistic and complicated FPGS scenarios, e.g., scheduling tasks with dependency, mixed-criticality task scheduling. We believe that there are many opportunities for leveraging advanced Deep Learning technologies to improve the quality of scheduling in various system configurations and problem scenarios.


5 Most essential skills to become a data scientist in 2021

#artificialintelligence

Data Science has become an emerging and hottest job role in 2020. With the increase in demand for skilled professionals, more and more people have started taking up data science course. If you want to become a data scientist in 2021, you need to develop a set of skills. Here are the most essential skills to become a successful data scientist in near future. The latest version, Python 3 has become the default choice of language for data science.


Best Way to Learn Pandas

#artificialintelligence

This learner's guide will help you understand how to use the features of pandas for interactive data manipulation and analysis. This book is your ideal guide to learning about pandas, all the way from installing it to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights. You start with an overview of pandas and NumPy and then dive into the details of pandas, covering pandas' Series and DataFrame objects, before ending with a quick review of using pandas for several problems in finance. With the knowledge you gain from this book, you will be able to quickly begin your journey into the exciting world of data science and analysis. Install pandas on Windows, Mac, and Linux using the Anaconda Python distribution Learn how pandas builds on NumPy to implement flexible indexed data Adopt pandas' Series and DataFrame objects to represent one- and two-dimensional data constructs Index, slice, and transform data to derive meaning from information Load data from files, databases, and web services Manipulate dates, times, and time series data Group, aggregate, and summarize data Visualize techniques for pandas and statistical data Adopt pandas' Series and DataFrame objects to represent one- and two-dimensional data constructs Adopt pandas' Series and DataFrame objects to represent one- and two-dimensional data constructs Michael Heydt is an independent consultant, educator, and trainer with nearly 30 years of professional software development experience, during which time, he focused on Agile software design and implementation using advanced technologies in multiple verticals, including media, finance, energy, and healthcare.


Introduction to Time Series Analysis in Python - KDnuggets

#artificialintelligence

A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. So any dataset in which is taken at successive equally spaced points in time.


A Comprehensive Guide to Metis Data Science Bootcamp

#artificialintelligence

I have recently graduated from the Metis Data Science Bootcamp (Singapore, Batch 5), and enrolling in the Bootcamp might have been one of the best decisions that I have ever made in my life. Out of the mandatory 5 projects that I have completed, all have been published on Towards Data Science (TDS), and 2 have been featured on its social media. Most importantly, however, I managed to land myself two job offers as Data Scientist even before the Bootcamp concluded. Therefore, I wish to share with aspiring data scientists on the Bootcamp, the pros and cons of it, and how to leverage on it to derive the maximum benefits. In summary, Metis Data Science Bootcamp is an accredited 12-weeks project-based and immersive apprenticeship in full-stack data science.


An Ultimate Guide to Time Series Analysis in Pandas

#artificialintelligence

It is the analysis of the dataset that has a sequence of time stamps. It has become more and more important with the increasing emphasis on machine learning. So many different types of industries use time-series data now for time series forecasting, seasonality analysis, finding trends, and making important business and research decisions. So it is very important as a data scientist or data analyst to understand the time series data clearly. I will start with some general functions and show some more topics using the Facebook Stock price dataset. Time series data can come in with so many different formats. But not all of those formats are friendly to python's pandas' library.


Do's and Don'ts of Analyzing Time Series - KDnuggets

#artificialintelligence

When handling time series data in your Data Science analysis work, a variety of common mistakes are made that are basic, but very important, to the processing of this type of data. Here, we review these issues and recommend the best practices.


An Ultimate Guide to Time Series Analysis in Pandas

#artificialintelligence

It is the analysis of the dataset that has a sequence of time stamps. It has become more and more important with the increasing emphasis on machine learning. So many different types of industries use time-series data now for time series forecasting, seasonality analysis, finding trends, and making important business and research decisions. So it is very important as a data scientist or data analyst to understand the time series data clearly. I will start with some general functions and show some more topics using the Facebook Stock price dataset. Time series data can come in with so many different formats. But not all of those formats are friendly to python's pandas' library.


Pandas on Steroids: End to End Data Science in Python with Dask - KDnuggets

#artificialintelligence

As the saying goes, a data scientist spends 90% of their time in cleaning data and 10% in complaining about the data. Their complaints may range from data size, faulty data distributions, Null values, data randomness, systematic errors in data capture, differences between train and test sets and the list just goes on and on. One common bottleneck theme is the enormity of data size where either the data doesn't fit into memory or the processing time is so large(In order of multi-mins) that the inherent pattern analysis goes for a toss. Data scientists by nature are curious human beings who want to identify and interpret patterns normally hidden from cursory Drag-N-Drop glance. Even after answering these questions, multiple sub-threads can emerge i.e can we predict how the Covid affected New year is going to be, How the annual NY marathon shifts taxi demand, If a particular route if more prone to have multiple passengers(Party hub) vs Single Passengers( Airport to Suburbs).