AI

Source: analyticsvidhya.com
The Jupyter Notebook is a very famous web-based interactive computing platform, which receives love from many of us. However, there’s a latest web-based interactive development environment for notebooks, code, and data - JupyterLab - which is worth a chance to check. You will love your transition to JupyterLab to perform your data science tasks, trust me. In this article, we will introduce you some of the updates and changes that you can see in JupyterLab 3.0, the latest version of it.
18 Jan 2022

What’s new in JupyterLab 3.0?

The Jupyter Notebook is a very famous web-based interactive computing platform, which receives love from many of us. However, there’s a latest…

Source: medium.com
IDEs generally have extensive collections of features to make the lives of programmers considerably easier. So in this article, let's see some recommendations of IDEs for 4 most frequently used programming languagues: R, Python, Scala, and Julia.
09 Jan 2022

Top 8 IDEs for Machine Learning and Data Science (Part 2)

IDEs generally have extensive collections of features to make the lives of programmers considerably easier. So in this article, let’s…

The exchange of data between corporations is known as data monetization. It is the process of earning income or creating new revenue streams by utilizing data, which is estimated to support expansion of the global data monetization market. Direct data monetization as well as indirect data monetization is the two forms of data monetization. The sale of raw data is known as direct data monetization. Companies are making income directly from the sale of data in this scenario. Selling a company's analysis, bartering or trading data, and implementing one or more APIs are all examples of direct data monetization. Companies leverage their data to have a quantifiable effect on indirect monetization. Indirect monetization aids businesses in lowering costs, increasing productivity and efficiency, developing new goods or services, and discovering new consumer types or company categories, to name a few benefits.
05 Jan 2022

Using Cloud and AI Technologies to Make Data Driven Decisions For Monetization

The exchange of data between corporations is known as data monetization. It is the process of earning income or creating…

Source: medium.com
IDEs generally have extensive collections of features to make the lives of programmers considerably easier. So in this article, let's see some recommendations of IDEs for 4 most frequently used programming languagues: R, Python, Scala, and Julia.
04 Jan 2022

Top 8 IDEs for Machine Learning and Data Science (Part 1)

IDEs generally have extensive collections of features to make the lives of programmers considerably easier. So in this article, let’s…

Source: upgrad.com
The tools data scientists use constantly change, unlike the skills required remain the same. In this article, we will share best basic tools for academic data scientists—but also for early career data scientists and even non-programmers looking to employ data science techniques into their workflow.
28 Dec 2021

Some effective tools Data Scientists need to know

The tools data scientists use constantly change, unlike the skills required remain the same. In this article, we will share…

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. TensorFlow 2.7 is released and improves usability with clearer error messages, simplified stack traces, and adds new tools and documentation for users migrating to TF2.
22 Dec 2021

What’s new in TensorFlow 2.7?

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and…

IT owes its existence as a professional discipline to companies seeking a competitive edge from information. Today, organizations are awash in data, but the technology to process and analyze it often struggles to keep up with the deluge of every machine, application and sensor emitting an endless stream of telemetry. An explosion in unstructured data has proved to be particularly challenging for traditional information systems based on structured databases, which has sparked the development of new algorithms based on machine learning and deep learning. This, in turn, has led to a need for organizations to either buy or build systems and infrastructure for machine learning, deep learning and AI workloads.
15 Dec 2021

Infrastructure requirements for AI and machine learning

IT owes its existence as a professional discipline to companies seeking a competitive edge from information. Today, organizations are awash…

These terms artificial intelligence, machine learning, deep learning, and neural networks are often used in our conversation, but what is the difference between them? In this article, let's clarify it.
14 Dec 2021

The difference between AI vs. Machine Learning vs. Deep Learning vs. Neural Networks

These terms artificial intelligence, machine learning, deep learning, and neural networks are often used in our conversation, but what is the difference…

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