By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks. This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. * A free license to install MATLAB for the duration of the course is available from MathWorks.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, we will add a reCAPTCHA checkbox to a website.

This Guided Project "Continuous Delivery and Release Pipelines with Azure DevOps" is for IT professionals who want to raise the quality of their software products to a higher level by totally automating their software applications’ deployment processes. In this 1-hour long project-based course, you will learn how to use Azure DevOps Services to safely deploy new software version to the environments in the Microsoft’s cloud called Azure. Since this project uses Azure DevOps Services, you will need access to an Azure DevOps account. In the video at the beginning of the project you will be given instructions on how to sign up for one. Besides that, you need to have a valid account on Microsoft Azure and a Resource group which will be used across this guided project, so you will need to have that prepared before you begin. If you are ready to start automating deployment process of your applications, then this project is for you! Let’s get started!

You should complete the VLSI CAD Part I: Logic course before beginning this course. A modern VLSI chip is a remarkably complex beast: billions of transistors, millions of logic gates deployed for computation and control, big blocks of memory, embedded blocks of pre-designed functions designed by third parties (called “intellectual property” or IP blocks). How do people manage to design these complicated chips? Answer: a sequence of computer aided design (CAD) tools takes an abstract description of the chip, and refines it step-wise to a final design. This class focuses on the major design tools used in the creation of an Application Specific Integrated Circuit (ASIC) or System on Chip (SoC) design. Our focus in this part of the course is on the key logical and geometric representations that make it possible to map from logic to layout, and in particular, to place, route, and evaluate the timing of large logic networks. Our goal is for students to understand how the tools themselves work, at the level of their fundamental algorithms and data structures. Topics covered will include: technology mapping, timing analysis, and ASIC placement and routing. Recommended Background: Programming experience (C, C++, Java, Python, etc.) and basic knowledge of data structures and algorithms (especially recursive algorithms). An understanding of basic digital design: Boolean algebra, Kmaps, gates and flip flops, finite state machine design. Linear algebra and calculus at the level of a junior or senior in engineering. Elementary knowledge of RC linear circuits (at the level of an introductory physics class).

Ongoing and proactive management is critical to the security and performance of database management systems. Database administration is the function of managing the operational aspects of database systems and maintaining them. Database administrators work to ensure that applications make the most efficient use of databases and that physical resources are used adequately and efficiently. In this course, you will discover some of the activities, techniques, and best practices for managing a database. You will learn about configuring and upgrading database server software and related products. You will also learn about database security; how to implement user authentication, assign roles, and assign object-level permissions. You will also gain an understanding of how to perform backup and restore procedures in case of system failures. You will learn about how to optimize databases for performance, monitor databases, collect diagnostic data, and access error information to help you resolve issues that may occur. Many of these tasks are repetitive, so you will learn how to schedule maintenance activities and regular diagnostic tests and send automated messages of the success or failure of a task.

One of the biggest challenges QAs and Developers face in test automation is synchronizing application under test and test automation code. Selenium provides multiple wait methods (like Implicit and Explicit waits) to synchronize the application under test and test automation code. In this two hours guided project, through hands-on, practical experience, you will go through concepts using Page load timeout, usage of implicit, explicit and Fluent waits.

Explore the C and C++ languages. Look at the specificity of the C/C++ languages and how this impacts security, ways C/C++ can interact with the external world, error handling, the execution environment and much more.

In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.

In this 2-hour long project-based course, you will learn how to train an Object Detection Model using Facebook's Detectron2. Detectron2 is a research platform and a production library for deep learning, built by Facebook AI Research (FAIR). We will be building an Object Detection Language Identification Model to identify English and Hindi texts written which can be extended to different use cases. We will look at the entire cycle of Model Development and Evaluation in Detectron2. We will first look at how to load a dataset, visualize it and prepare it as an input to the Deep Learning Model. We will then look at how we can build a Faster R-CNN model in Detectron2 and customize it. We will then configure the parameters & hyperparameters of the model. We will then move on to training the Model and subsequently to model inference and evaluation. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you use Cloud Functions and Pub/Sub to create an end-to-end document processing pipeline using Document AI. The Document AI API is a document understanding solution that takes unstructured data, such as documents and emails, and makes the data easier to understand, analyze, and consume. In this lab, you will create a document processing pipeline that will automatically process documents that are uploaded to Cloud Storage. The pipeline consists of a primary Cloud Function that processes new files that are uploaded to Cloud Storage using a Document AI form processor and then saves form data detected in those files to BigQuery. If the form data includes any address fields the address data is then written to a Pub/Sub topic that in turn triggers a second Cloud Function that uses to Geocoding API to provide geographic coordinate data for the address that is also written to BigQuery. This is a simple pipeline that uses a general form processor that will detect basic form data, such as a labelled field containing address information. Document AI processors that use one of the specialized parsers that are beyond the scope of this lab provide enhanced entity information for specific document types even when those documents do not include labelled fields. For example, a Document AI Invoice parser can provide detailed address and supplier information, from an unlabelled invoice document because it understands the layout of invoices.