The Intel® Distribution of OpenVINO™ toolkit helps developers achieve fast, accurate, real-world results using high-performance AI and computer vision inference deployed from the edge to the cloud.
In this course, you will learn to create high-performance, deep learning applications easily with a streamlined development workflow. You will learn about running inference tools for low precision optimizations, computer vision libraries, media processing, and pre-optimized kernels. We will also teach you how to quickly deploy your AI applications productively across combinations of host processors and accelerators, including CPUs, GPUs, and VPUs, on-prem, on-device, and in the browser or cloud using the write once deploy anywhere approach.
Throughout this course, you will learn how developers use the OpenVINO™ toolkit on multiple Intel® architectures to enable new and enhanced use cases across industries, including manufacturing, health and life sciences, retail, security, and more.
This course with OpenVINO™ toolkit is focused on developing deep learning inference applications and not model training. OpenVINO™ toolkit provides a set of pre-trained models that you can use for learning and demo purposes or for developing deep learning software.
With this certification course you will also get complimentary access to CloudxLab courses on Neural Networks and Computer Vision which will help you in gaining expertise in developing Computer Vision applications using Python*, Keras*, TensorFlow*, OpenCV* and Convolutional Neural Networks.
Intel creates world-changing technology that enables global progress and enriches lives. By embedding intelligence in the cloud, network, edge, and every kind of computing device, we enable developers to unleash the potential of data to transform business and society for the better. Our legacy of democratizing technology continues by making it simpler, faster, and more cost-effective for everyone to infuse AI into their apps, everywhere that computing takes place.
The Intel® Distribution of OpenVINO™ toolkit is a software tool that helps developers harness the full potential of AI to deploy high-performance deep learning applications on multiple processors, accelerators, and environments with a write once, deploy anywhere efficiency. Partner with us to deliver cutting-edge solutions by leveraging rapidly advancing hardware technologies with Intel® software.
CloudxLab is a team of developers, engineers, and educators passionate about building innovative products to make learning fun, engaging, and for life. We are a highly motivated team who build fresh and lasting learning experiences for our users. Powered by our innovation processes, we provide a gamified environment where learning is fun and constructive. From creative design to intuitive apps we create a seamless learning experience for our users. We upskill engineers in deep tech - make them employable & future-ready.
Learn how to use the Intel® Distribution of OpenVINO™ toolkit for Image Classification, Object Detection, and Neural Style Transfer
Develop Speech Recognition, Text Recognition and Natural Language Processing applications
Build Deep Learning application using multiple models in one application and run inference on various Intel® architecture platforms including CPU, iGPU, VPU with Write Once Deploy Anywhere approach
Create an application to count the number of people who are violating the safety gear standards like not wearing safety jackets and hard-hats
Profile your neural network on your hardware configuration, as well as connect to targets in your local network and profile on them remotely. Use the Benchmark App to estimate the inference performance of your deep learning model on various devices.
Design a healthcare application by classifying the probability of pneumonia in X-ray images
Creates an end-to-end pipeline to detect the presence of COVID-19 preventive measures, such as social distancing using computer vision inference.
Monitor traffic intersections via IP cameras to optimize traffic flow. Detect vehicles and pedestrians, record vehicle types and counts, calculate velocity and acceleration, and more.
Extend the cloud applications to seamlessly develop and deploy solutions at the edge
Churn the mail activity from various individuals in an open source project development team.
Build a CNN from scratch to classify FashionMNIST data using Tensorflow* 2, Matplotlib* and Python*.
Learn to train a neural network from scratch to classify data using TensorFlow* 2.
Learn to use the custom defined loss functions along with TensorFlow* 2
Learn how to access the pre-trained models(here we get pre-trained ResNet model) from Keras* of TensorFlow* 2 to classify images.
Build a basic neural network to classify if a given image is of cat or not using transfer learning technique with Python* and Keras*.
Learn how to read a pre-trained TensorFlow* model for object detection using OpenCV.
Use TensorFlow* 2 to generate an image that is an artistic blend of a content image and style image using Neural Style Transfer.
Senior Software Developer at Decision Resources Group
This course will guide you:
• To develop Deep Learning applications seamlessly.
• Optimize and improve performance with and without external accelerators
• Run high-performance inference with a “Write Once and Deploy Anywhere” approach.
• Deploy your same application across combinations of host processors, accelerators, and environments, including CPUs, GPUs, VPUs, on-premise and on-device, and in the browser or in the cloud.
• Intel® will cover advanced features and tools provided by the OpenVINO™ toolkit. You will learn how to utilize those tools to help you identify the best inference configuration for your needs.
• You will develop, test, and run your AI solution on a cluster of the latest Intel® hardware and software with Intel® DevCloud for the Edge.
• This course is focused on developing deep learning inference applications and not model training. The OpenVINO™ toolkit provides a set of pre-trained models that you can use for learning and demo purposes or for developing deep learning software.
Post completion of this course you will be able to develop and deploy high performance Deep Learning applications at the Edge. This course will prepare you for roles such as an IoT Developer/Engineer, Deep Learning Engineer, AI Specialist and more for companies innovating in AI.
You should expect to allocate about 3 hours/week for the duration of 3-4 months to complete this course.
You will learn to create Deep Learning applications for Industrial, Retail, Smart City, and other verticals to solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing and more using Intel® Distribution of OpenVINO™ toolkit. After completing this course, you can also try many other tutorials and reference implementations on Intel® Devcloud and Intel® Edge Software Hub. Kindly refer to the course curriculum for more information.
In this course, we will go through the installation process on a local machine and you will also be provided access to Intel® DevCloud for the Edge to learn and build AI applications remotely using a web browser. You will find instructions to get your 90 days access (which you can extend further by following the process as described in the course videos) to Intel® DevCloud for the Edge.
For the complimentary courses by CloudxLab, we will provide you with 180 days of access to CloudxLab's online lab so that you do not have to install anything on your local machine.
No, this course is for everyone. The complimentary access to CloudxLab courses will help you in learning the required foundations to make the most out of this certificate course.
In this course, you will get two certificates - One from Intel and another one from CloudxLab
To earn the certificate from Intel, you are required to complete at least 80% of the course content along with mandatory projects. The above requirement needs to be met within 180 days of enrollment in order to be eligible for the certificate from Intel.
To earn the certificate from CloudxLab, you are required to complete at least 80% of the course content along with mandatory projects. The above requirement needs to be met within 180 days of enrollment in order to be eligible for the certificate from CloudxLab.
The course access will be for 6 months. To earn the certificate, you are required to complete the course within the deadlines.
If you are unable to complete the course within the stipulated deadlines then you can enroll in the course again to earn the certificate.
If you are unhappy with the product for any reason, let us know within 7 days of purchasing or upgrading your account, and we'll cancel your account and issue a full refund. Please contact us at email@example.com to request a refund within the stipulated time. We will be sorry to see you go though!