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Replika is a chatbot that doesn’t require enormous amounts of datasets. Instead, all it needs is you.
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Customer experience is undergoing tremendous changes right now. Due to the explosion of smart technology, the biggest amount of our…
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Data warehousing is one of the hottest topics both in business and in data science. But if you’re new to the field, you’re probably wondering what a data warehouse is, why we need it, and how it works. Don’t worry because, in this article, you’ll find the answers to all these questions.
First, let’s start with a definition: the meaning of the phrase: ‘Single source of truth’.
In information systems theory, the ‘single source of truth’ is the practice of structuring all the best quality data in one place.
Here’s a very simple example.
Surely it has happened to you to work on a file and to create many different versions of it.
Well, once you are ready you often place the word ‘final’ at the end. This results in having a bunch of files with extensions:
Or my favorite:
If this is you, you are not alone. It seems that even corporations never know where the most recent or most appropriate file is.
That would be quite helpful wouldn’t it?
Well, a data warehouse exists to fill that need.
It is the place where companies store their valuable data assets, including customer data, sales data, employee data, and so on.
In short, a data warehouse is the de facto ‘single source of data truth’ for an organization. It is usually created and used primarily for data reporting and analysis purposes.
It is:
Let’s quickly go through these, one by one.
Therefore, it does not contain all company data ever, but only the subject matters of interest. For instance, data on your competitors need not appear in a data warehouse, however, your own sales data will most certainly be there.
Each database, or each team, or even each person has their own preferences when it comes to naming conventions. That is why common standards are developed to make sure that the data warehouse picks the best quality data from everywhere. This relates to ‘master data governance’, but that is a topic for another time.
As said before, we mainly use a data warehouse for analysis and reporting, which implies we need to know what happened 5 or 10 years ago.
Once there, it cannot be changed or deleted.
Often it is aggregated or segmented in some ways, in order to facilitate analysis and reporting.
So, that’s what a data warehouse is – a very well structured and nonvolatile, ‘de facto’, single source of truth for a company.
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Nikola Pulev is a Natural Sciences graduate from the University of Cambridge, in the UK, turned Data Science practitioner and a course instructor at 365 Data Science. Nikola has a passion for Mathematics, Physics, and Programming. Over the years, he has taken part in multiple national and international competitions, where he has won numerous awards. He also holds a silver medal from the International Physics Olympiad.
Hi, my name is Nikola and I am a Cambridge Physics graduate turned data scientist.
Well, I am part of the 365 Data Science Team in the role of a course creator. Course creation is, in general, a collaborative effort. There are lots of people involved in the creation of each course. Basically, my part is to do continuous in-depth research of the topic and use my expertise to write the content of the course.
So far, I have developed the Web Scraping and API Fundamentals course along with Andrew Treadway. This was a very exciting project for me, as it was my first course for the company.
Well, the project I am currently working on is a continuation on our Deep Learning course, as it delves into Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This is an extensive topic, though, and the project is still in development.
Although our courses are online, the students have the opportunity to ask all the questions they have in the Q&A Hub. I do my best to answer those questions as thoroughly as possible. However, in my opinion, a good teacher should aim to answer the question a student may have before the student themselves asks it. So, in my courses, I constantly try to detect the stuff that may be confusing and clarify them in more detail.
So, I have graduated from the University of Cambridge as a Natural Scientist, although I mainly specialized in Physics. I have always had a passion for mathematics and science in general, as well as coding. Not necessarily as a programmer, but I do like to create little programs to solve problems – whether it would be to automate some tasks or to create simulations.
The years in university and Physics in general, have taught me to think analytically and to research topics extensively. Also, I have a circle of friends that are into science as well, and we frequently exchange knowledge. So, in that regard, I am practicing both my analytical and my teaching skills.
When I graduated, I wanted to do something that would include mathematics and coding and that can be applied in practice. Data science seems to provide a good mixture of those. But that was not the only factor in my decision
365 Data Science was the main factor – they provided me with the opportunity to contribute to society in a positive manner. And teaching makes me feel accomplished in so many ways!
As for the team itself, they are very professional and great group of people!
Absolutely. Shoot!
80% is for the weak.
Obviously the 100 duck-sized whales – they don’t have teeth or beaks and they are only in water.
I would ban alarm clocks and make it so that there is no need to wake up early (I have to take care of my fellow night owls after all). Then, I would wake up to the sound of my alarm and realize it was all just a dream – too good to be true…
Recently, a friend of mine dropped the bombshell that vectors can be considered as a generalization of tensors and not the opposite, which is mindboggling. Normally, I wouldn’t go around scaring people with this info, but since you asked…
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At the time of developing the AI models through machine learning (ML) first and most important thing you need, relevant training data sets, which can only help the algorithms understand the scenario through new data or seeing the objects and predict when used in real-life making various tasks autonomous.
In the visual perception based AI model, you need images, containing the objects that we see in our real life. And to make the object of interest recognizable to such models the images need to be annotated with the right techniques.
And image annotation is the process, used to create such annotated images. The applications of image annotation in machine learning and AI is substantial in terms of model success.
So, right here we will discuss about the applications of the image annotation, but before we proceed, we need to review the definition of image annotation and its use in AI industry. Image annotation is the process of making the object of interest detectable and recognizable to machines.
And to make such objects recognizable in the images, they are annotated with added metadata for the description of the object. And when a huge amount of similar data is feed into the model, it becomes trained enough to recognize the objects when new data is presented in real-life situations.
Annotated images are mainly used to make the machine learn how to detect the different types of objects. But as per the AI model functions, ML algorithms compatibility and use in the various industries, image annotation applications also differ that all about we will discuss here below with the annotation types.
The most important application of image annotation is detecting the objects in the images. In an image, there are multiple things, or you can say objects, but every object would be not required to get noticed by the machines. But the object of interest need to be get detected, and the image annotation technique is applied to annotate and make such objects detectable through computer vision technology.
After detecting the object, it is also important to recognize what types of objects it is, humans, animals or non-living objects like vehicles, street poles and other man made objects visible in the natural environment. Here again image annotation helps to recognize the objects in the images.
3. Real vs Fake Tweet Detection using a BERT Transformer Model in few lines of code
Though, object detection and recognition runs simultaneously, and while annotating the objects in various cases, the notes or metadata is added to describe the attributes and nature of the object, so that machine can easily recognize such things and store the information for the future references.
It is not necessary all objects in an image belong to the same category, if a dog is visible with man, it needs to be classified or categorized to differentiate both of them. Classification of the objects in the images is another important application of image annotation used in machine learning training.
Along with image classification, the localization of objects is also done through image annotation practice. In image annotation, there are multiple techniques, used to annotate the objects and classified into the different categories helping the visual perception based AI model detect and categorize the objects.
Just like object classification, objects in the single class need to be segmented to make it more clear about the object, its category, position and its attributes. Semantic segmentation image annotation is used to annotate the objects with each pixel in the image belongs to a single class.
The main applications of image annotation is to make the AI model or machine learning algorithm learn with more accuracy about objects in the images. For semantic segmentation, image annotation is basically applied for deep learning-based AI models to give precise results in various scenarios.
AI cameras in smartphones or security surveillance, are now able to recognize the face of humans. And do you how it became possible in AI world? Thanks to image annotation, that makes the humans faces recognizable through computer vision with the ability to identify the person from the database and discriminate them among the huge crowd from the security surveillance system perspective.
In image annotation for face recognition algorithms, the faces of humans are annotated from one point to another point measuring the dimension of the face and its various points like chin, ears eyes, nose and mouth. And these facial landmarks are annotated and provided to the image classification system. Hence, image annotation is playing another important role in recognizing the people from their faces.
I hope you got to know the applications of image annotation in the world of AI and machine learning. Now you should know what are the types of image annotations used to create the machine learning training datasets for deep learning based AI models? And we will also discuss here the application of different types of image annotation into various industries, fields and sectors with uses cases of AI-based models.
Bounding box annotation is one of the most popular techniques used to detect the objects in the images. The object of interest are annotated either in a rectangular or square shape to make the object recognizable to machines through computer vision. All types of AI models like self-driving cars, robots, autonomous flying objects and AI security cameras relying on data created by bounding box annotation.
To recognize, classify and segment the objects in the single class, semantic image segmentation is used to annotate the objects for more accurate detection by machines. It is actually, the process of diving the images into multiple segments of an object having the different semantic definitions. Autonomous vehicles and drones, need such training data to improve the performance of the AI model.
The image annotation applications not only include object detection or recognition, even can also measure or estimate the types and dimensions of the object. 3D point cloud annotation is the technique helps to make such objects detectable to machines through computer vision. Self-driving cars are the use case, where training data sets is created through 3D point cloud annotation. This image annotation helps to detect the object with additional attributes including lane and sideways path detection.
Landmark annotation is another type of image annotation techniques used to detect human faces. The AI models like in security surveillance, smartphones and other devices can detect the human faces and recognize the gestures and various human possess. Landmarking is also used in sports analytics to analyze the human possess performed while playing outdoor games. Cogito provides the landmark point annotation with the next-level of accuracy for precise detection of human faces or their poses.
Detecting the dimensions of the object is also important for AI models to get a more accurate measurement of various objects. The 2D images are annotated with capturing all the dimensions visible in the image to build a ground truth dataset for 3D perception on the objects of interest. Again autonomous vehicles, and visual perception models used to detect the indoor objects like carton boxes with the dimension need such annotated images, created through 3D cuboid annotation.
Similarly, polygon annotation is used to annotate the objects that are in irregular shapes. Coarse or asymmetrical objects can be made recognizable through the polygon image annotation technique. Mainly road marking or other objects are annotated for the self-driving cars. And autonomous flying objects like drones, viewing the objects from ariel view can detect or recognize such things when trained with training data sets created through polygon annotation for precise object detection.
Lines, Polylines and Splines are all similar types of image annotations used to create the training data sets allowing computer vision systems to consider about the divisions between important regions of an image.
The boundaries, annotating lines or splines is useful to detect lanes for self-driving cars. Road surface marking that are indicating the instructions of driving on the road need to also make understandable to autonomous cars. Polyline annotation that divides one region from another region.
The right applications of image annotation is possible when you use the right tools and techniques to create high-quality training data sets for machine learning.
Cogito is the industry leader in human-powered image annotation services with the best level of accuracy for different AI models or use cases. Working with a team of well-trained and experienced annotators, can produce the machine learning training data sets for healthcare, agriculture, , automotive, drones and .
What Are The Applications of Image Annotation in Machine Learning and AI? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
Over the past two halves, Integrity teams across a platform could come into alignment on one central idea: there should be more…
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