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With Intel Neural Compute Stick, DeepLearning is now available on any platform with a USB port

Intel has recently announced the release of its new product, the Intel Neural Compute Stick. This new USB-based device allows you to experiment with deep learning on any platform with a USB port, including laptops, desktops and even Raspberry PI platforms. It contains an Atom processor, and it supports Linux-based deep learning frameworks such as Caffe and Torch through OpenCV (Open Computer Vision) libraries. In this article, I will explain how to install and use the Intel Neural Compute Stick as well as show you how to get started with DeepLearning using your computer or Raspberry PI platform in under 10 minutes! 

Introducing the Intel® Neural Compute Stick 2 (Intel® NCS2) 

A small form factor, low-power AI accelerator that allows you to prototype and develop your AI application. It’s packed with an array of developer resources such as open source frameworks, sample codes and tutorials. It’s also designed to be compatible with popular deep learning frameworks such as Caffe*, MXNet*, TensorFlow* and PyTorch*. If you are an early adopter or already building systems based on first generation Intel NCS (Intel® NCS 1), there’s no need for new code; simply upgrade to second generation module for performance boost. As always, we welcome feedback from our community. 

What is Deep Learning? 

Deep learning refers to an artificial neural network that has many layers and whose structure allows it to process data in a way similar to biological neural networks. The first deep learning applications were written using software libraries such as Google’s TensorFlow or Microsoft’s CNTK (Cognitive Toolkit). These libraries handle all of the details of compiling, optimizing and deploying artificial neural networks onto platforms like Microsoft Azure and Amazon Web Services. But sometimes you need to prototype your own models. For example, perhaps you have unique constraints due to privacy requirements or other factors that prevent you from hosting your data in the cloud. In these instances, it can be beneficial for you to roll your own custom model from scratch – especially when working with small amounts of data. 

Intro to Machine Learning 

Machine Learning (ML) and Artificial Intelligence (AI) are popular buzzwords. They’re being used by both startups and Fortune 500 companies to try to stay ahead of the latest trends and market themselves as cutting-edge. Machine learning applies algorithms that allow computers to find patterns in large amounts of data without requiring explicit instructions from humans. While you may be familiar with some of its specific terms or applications, such as Natural Language Processing or Deep Learning, it can be difficult to understand how all of these different things fit together into a whole system. Today we’re going to talk about some basic models that make up machine learning systems and walk through an example so you can get your own taste for machine learning algorithms in action! 

Designing your own model 

Designing your own neural network is not easy. You need to carefully consider many parameters before you even start coding your network. Choosing which learning algorithm to use may be as simple as looking up online references and research papers but choosing optimal hyperparameters for that algorithm may take weeks of experimentation (and keep changing as you learn more about how different models work). The key takeaway here is that designing deep neural networks can be easy if you know what parameters matter or difficult if you don’t. Don’t let its intimidating form scare you away from building your own models; it just takes a little bit of preparation and practice. And once you have some experience designing your own networks, you’ll be amazed at how quickly they converge! 

Running code 

You don’t need to buy an expensive development environment for creating models with deep learning. It can be done in your browser using frameworks like TensorFlow.js and Caffe2. For example, in TensorFlow.js you can use JavaScript to define how you want to train a model and run that training from within your browser – no Python or GPU required! This code will turn your browser into an inference engine and allow it to execute trained neural networks instantly, so even if you are not interested in learning about all of these frameworks – something changed in how we develop deep learning models and what we use them for…you probably should know about it! 

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