Certificate Course on Developing Deep Learning Applications by Intel via CloudxLab: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

The Certificate Course on Developing Deep Learning Applications is a self-paced 6 months course that is jointly developed, and also offered by the institutes' Intel Corporation, and ClouxLab. The candidates can start learning directly from the engineers of Intel, and the deep learning industry experts. With the syllabus of the course, one can learn how the developers can make use of the OpenVINO™ toolkit across industries

Certificate Course on Developing Deep Learning Applications training will be focused on developing different deep learning applications that can be used for demo stages, or learning stages of development of deep learning software. After the 2 certificates are offered the students will gain knowledge, and the expert skills for creating, and developing Computer Vision applications with the use of tools like Keras, OpenCV, Python, and Convolutional Neural Networks.

Course and certificate fees

Fees information

₹ 4,999

The Certificate Course on Developing Deep Learning Applications fee is only 4,999 along with Additional Taxes Applicable (If Any).

Certificate Course on Developing Deep Learning Applications Fee Structure

Description	Amount in Rs
Programme Fee	4,999

certificate availability

Yes

certificate providing authority

CloudxLab +1 more

Who it is for

If one is interested in concepts of deep learning application then this course will definitely interest them.

Eligibility criteria

Certification Qualifying Details

The Developing Deep Learning Applications certification by Intel and CloudxLab shall be offered separately. To get both the certificates, 80% completion of the course, and projects are necessary which must be within 180 days of enrollment.

What you will learn

Knowledge of deep learning

After the completion of the Certificate Course on Developing Deep Learning Applications syllabus, every student will be able to deliver high performance in applying techniques of deep learning. The students will also learn to deploy AI applications on different processors like GPUs, CPUs, and VPU in the cloud or browser. The students will be able to use these applications throughout different sectors like health, life sciences, security, manufacturing, retail, and more.

The syllabus

Module 1: Introduction to AI and OpenVINO™ Toolkit

AI market overview and use cases of Deep Learning
Deep Learning development cycle - History, performance, and motivation
Challenges in developing and deployment of Deep Learning applications at the Edge and while working with various Hardware Platforms
Intel® Distribution of OpenVINO™ toolkit overview, its components and development workflow
Creating an AI inference in few lines of code using OpenVINO™ toolkit
Simplify and Develop for the Edge with Intel® AI Ecosystem and the various hardware and software offerings

Module 2: Setting up the Development environment for AI with OpenVINO™ Toolkit

AI prototyping with OpenVINO™ toolkit using Intel® DevCloud for the edge
Run sample applications and evaluate performance on various Intel® architecture with Intel® Devcloud for the edge
OpenVINO™ toolkit installation and setup on a local machine
Getting started in 5 minutes with OpenVINO™ toolkit using Jupyter* Notebooks
OpenVINO™ toolkit opensource version- Clone, Compile and Contribute
Intel® Edge Software Hub, a one-stop resource for optimized software and offerings for key edge use cases

Module 3: Expedite development of high-performance deep learning inference applications with Intel® Open Model Zoo

Introduction to Open Model Zoo and download a pre-trained model for learning or for developing deep learning software
Analyze various pre-trained models and public models and select an appropriate model based on the use case
Tools overview - Model downloader and Accuracy checker
Demos demonstrating various use cases with pre-trained, and public models

Module 4: Optimization and Quantization of models for better performance

Introduction to Model Optimizer and understand its significance
Learn how model optimizer helps in streamlining the AI application development flow
Model conversion overview with Model Optimizer and understanding general and framework-specific conversion parameters
Generating Intermediate Representation files using Model Optimizer
Convert model with general and framework-specific conversion parameters
Understanding how the model optimizer works under the hood
Choosing the right Quantization option in Model Optimizer based on Hardware Platform and assessing the benefits and trade-offs of using different Quantization
Introduction to low precision optimization with Post-Training Optimization Tool and Understand the advantages of INT8 calibration
Learn various methods to boost model performance with Post-Training Optimization Tool

Module 5: Inference Engine & integration with deep learning applications

Understanding various Intel® architecture - CPU, iGPU, VPU to achieve accelerated performance
Introducing Intel® Movidius™ Myriad™ X VPU and getting started with Inference on Intel® Neural Compute Stick 2
Hardware agnostic inference with write once deploy anywhere approach
Creating powerful, scalable, and futureproof AI applications with a streamlined Development Workflow using Inference Engine
Performance improvement using SYNC and ASYNC modes of the Inference Engine
Efficient utilization of all the available compute resources using Heterogeneous and Multi Device Plug-ins to boost the performance

Module 6: Advanced Labs

Writing job file in Intel® Devcloud for the Edge and inference on multiple nodes
OpenVINO™ toolkit beyond vision- Speech, OCR, and NLP
Using the benchmark app to estimate the inference performance of your deep learning model on various devices
Advanced video analytics- Multiple models in one application
Local Project Migration with Intel® DevCloud for the Edge
Advanced samples and demos

Module 7: Streamline AI Application Development with Deep Learning Workbench

Introduction to Deep Learning Workbench and identifying the benefits
Learn to create optimized AI applications using a web-based graphical environment
Explore key concepts and features of Deep Learning Workbench
Getting familiar with Deep Learning Workbench workflow
Getting started with Deep Learning Workbench and identify the difference between running the Deep Learning Workbench on your local system and in the Intel® DevCloud for the Edge
Achieve the goal of optimized AI applications, whether you are new to deep learning or an advanced deep learning developer with Deep Learning Workbench
Develop and deploy optimized AI applications with Deep Learning Workbench

Module 8: Speed up the solution development journey with Intel® Edge Software Hub

Accelerate innovation for key edge computing use cases and starting with a powerful foundation for data processing, image processing, and edge AI analytics
Selecting the vertical-specific software package, customize the configurations to your needs, and decide on your target hardware for deployment
Explore the Reference Implementations to see how businesses are deploying edge intelligence in the real world
Downloading a Reference Implementation and Customizing it according to your requirements
Simplifying the edge-to-cloud workflow integration with Intel® Edge Software Hub

Admission details

For admission to the Certificate Course on Developing Deep Learning Applications classes, one may follow these steps:

Step 1: Visit the official site: https://cloudxlab.com/course/123/certificate-course-on-developing-deep-learning-applications-offered-by-intel-corporation

Step 2: After one gets to view the ‘Enroll Now’ button, they must be making a click.

Step 3: Then, creating a new cloudxlab account is mandatory.

Step 4: After the account gets created, the next procedure is fee payment.

Step 5: The confirmed admission is only based on the fee payment and the account creation.

Filling the form

To start off, account creation is the first step to applying to this programme. The accounts can be made by the learners using only Google accounts.

How it helps

Some Certificate Courses on Developing Deep Learning Applications benefits are:

The learners will also be asked to participate with international peers on real-world projects.
The candidates receive access that is complementary to exclusive CloudxLab deep-dive courses on Python, TensorFlow, OpenCV, Keras, and more.
The candidates are lucky to be certified by Intel.