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Introduction | AI Practitioners Guide for Beginners | Episode 1 | Intel Software

[youtube https://www.youtube.com/watch?v=XO-i6_lguP0&w=560&h=315]

Hi, I'm Beenish Zia, and that is AI Practitioners Guide for Beginners, the video series On this introductory episode, I present you an outline of what the guide covers, basics of AI, and business considerations of applying AI

It is a five-part video series And every episode gives you a sneak peek into what's covered in detail for that section in-the guide You possibly can read the written AI Practitioners Guide if in-the links Whether you might be someone who knows the fundamentals of AI or has taken courses online for machine learning, but have all the time wondered the place and how you can get began, the AI Practitioners Guide is a remarkable place to initiate The AI Practitioners Guide for Beginners will supply you with a high-level overview of business and data strategy that a machine learning practitioner must know, followed by an in depth walkthrough of how you can install and validate part of popular artificial intelligence frameworks, TensorFlow, on the Intel Xeon scalable platform

The guide details the steps for installing and running TensorFlow framework and examples in three distinct ways– bare metal, containers, or on the cloud So you’ll be able to select to try one or all three options of deployment, after which make an informed decision as to which option is smart for your business Now that we all know what is roofed in-the guide, let's speak about AI as a complete and have a look at the definition of AI The term "artificial intelligence" is continually evolving, but at its core, AI is about machines mimicking and sometimes exceeding cognitive functions related to the human intellect AI may be regarded as a large umbrella, a subset of machine learning

Machine learning may be defined as machine algorithms whose performance keeps improving as they're exposed to more data over time A subset of machine learning is Deep Learning, or DL, the place multilayered neural networks memorize from huge amounts of data Deep learning is the department of AI that has gained great popularity and adoption in recent times The nexus of the AI developments in-the approach coming is centered around deep learning with other approaches all playing meaningful roles depending on the data set, problem, and unique requirements The framework and examples if in-the guide are based on deep learning

DL comprises of two major pieces– training and inference Training teaches multilayered neural networks, besides often known as models These make it conceivable to identify things like objects or text That is done by feeding label data or content into the model Once the model is trained, inference begins using the training model to identify unlabeled content

Finally, let's cover some basic business considerations for AI Whether you're all for creating an AI-based application and applying it to your business, learning these business considerations are as meaningful as understanding the technical side of AI The business compulsory of AI is firmly rooted in data– the currency of the coming By 2020, we expect over 50 billion devices and 200 billion sensors to unite the net And this great explosion of smart and connected devices will result in incalculable volumes of data being generated

This data comprises extremely valuable insight for business, operations, and security that affected industrial really wish to extract, analyze, and interpret in real time Extracting value from that data requires all of the AI tools at our disposal The introductory step in your AI journey is to organize your data And for that, when thinking of an AI business model, it's meaningful to give attention to your entire data lifecycle You need to take into consideration creating, sourcing, transmitting, ingesting, and cleaning your data earlier you’ll be able to apply AI models to it

Doing this may help you create a market strenght end-to-end-optimized database solution in-the AI space Aside from having a solid data strategy, your introductory step must be to define the challenges you're facing across the organization and prioritizing them based on the business value and the way much it would price to unravel them The subsequent steps are to find out which AI approach is finest suited to every problem There are a couple of more steps you’ll have to consider and analyze, however the base line is whether you consider all these steps in-the AI life cycle, you'll stand a a lot better chance of realizing the business value that you just originally got down to deliver AI is revolutionizing the world

And to be part of it, it's meaningful to discern the fundamentals of TensorFlow on the Intel Xeon scalable platform I hope you'll unite me for your entire AI Practitioners Guide for Beginners, the video series Don't forget to take a look at the exhaustive written guide on the Intel Developer Zone linked in-the description Thanks for watching and protect developing [MUSIC PLAYING]

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