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Get Free GPU Online — To Train Your Deep Learning Model

Analytics Vidhya

Introduction You must have noticed that for training a very heavy deep learning model, you required a GPU which is mostly available at a very high cost.

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Microsoft’s Quest for the Next Killer App

insideBIGDATA

Microsoft’s investment into OpenAI was a clear move for the company to align itself with the next killer app that would drive engagement on Azure cloud.

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Top 10 AI and Data Science Trends in 2022

Analytics Vidhya

In this article, we shall discuss the upcoming innovations in the field of artificial intelligence, big data, machine learning and overall, Data Science Trends in 2022. Deep learning, natural language processing, and computer vision are examples […]. Times change, technology improves and our lives get better.

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TensorFlow vs. PyTorch: What’s Better for a Deep Learning Project?

Towards AI

Photo by Marius Masalar on Unsplash Deep learning. A subset of machine learning utilizing multilayered neural networks, otherwise known as deep neural networks. If you’re getting started with deep learning, you’ll find yourself overwhelmed with the amount of frameworks. Let’s answer that question.

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How to Become a Generative AI Engineer in 2025?

Towards AI

Generative AI is powered by advanced machine learning techniques, particularly deep learning and neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Cloud Computing: AWS, Google Cloud, Azure (for deploying AI models) Soft Skills: 1. Adaptability and Continuous Learning 4.

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Top 7 best Generative AI courses offered online   

Data Science Dojo

Image to Text with Azure Open AI: This combines the power of Azure and OpenAI to extract text from images. Smart Factory with Azure AI: This leverages Azure’s AI capabilities to optimize manufacturing processes and enhance productivity in smart factory environments.

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Remote Data Science Jobs: 5 High-Demand Roles for Career Growth

Data Science Dojo

Key Skills: Mastery in machine learning frameworks like PyTorch or TensorFlow is essential, along with a solid foundation in unsupervised learning methods. Stanford AI Lab recommends proficiency in deep learning, especially if working in experimental or cutting-edge areas.