A Tutorial Series for Software Developers, Data Scientists, and Data Center Managers
Headline-grabbing advances in artificial intelligence (AI), machine learning, and deep learning are transforming software as we know it. Established technology giants and fledgling startups alike are applying AI in new ways, such as self-driving cars, virtual personal assistants, discovery of new medications, or predicting financial market trends. The list of applications is long and varied, yet it barely scratches the surface of what’s to come.
In this tutorial series, experts in AI, machine learning, and deep learning familiarize you with AI tools, infrastructure, and techniques by demonstrating the process of building an application with powerful AI capabilities.
Using an automated movie-making app as an example, you will learn about two challenging and fundamental AI problems: image classification and sequence prediction. Discover how to use convolution neural networks for image classification by taking a set of images as input and automatically detecting emotions in those images. Next, you will learn to use recurrent neural networks to algorithmically synthesize a musical melody to accompany the emotions in the images. The final output is a finished movie complete with a computer-generated soundtrack.
Throughout the series, we illustrate various AI concepts and introduce you to Intel® architecture that supports deep neural networks. We show you how to help streamline the AI app-coding process using modern technologies from Intel including:
We review the key phases in the AI development process: ideation, team formation, data collection and storage, model development and evaluation, and deployment. We also evaluate all of the critical decision points by comparing various AI algorithms and frameworks, as well as data center and cloud infrastructure options, just as you would while working on your own app.
These tutorials assume an intermediate knowledge of the Python* programming language, basic linear algebra, basic statistics, and basic probability theory, as well as a familiarity with GitHub*. Even if you do not have these skills, you can sign up and follow along as we share source code and environments suitable for cloning to build AI apps of your own. Non-technical readers can gain insights and details on how to develop an AI application. We even introduce you to Docker* containers, the Keras* neural network API, the TensorFlow* software library for machine intelligence, and the Caffe* deep learning framework.
Everyone is encouraged to use these tutorials, but they were written primarily for the following professionals:
The complete Hands-On AI tutorial series is divided into five phases. Each phase will address multiple focus areas:
Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
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