Deep Learning With Big Analytics Under A Quasi-Open Set
The present and the future are completely driven by technology. You can find a number of changes that are taking place around us, and one of the developments that we have seen is big data. But before heading to understand this, it's important to know that data is the key driver of change for different industries. You can find a number of companies investing in optimally using data, and one such technology that we are using at its best is big data. It has been one of the most revolutionary technologies of the present, which is going to pave the way for a strong future.
The big future for big data awaits us, and it is taking place at a breakneck pace. The big data market is expected to grow with a CAGR of 53.7% during the period of 2015- 2022. The market size of big data is expected to reach $100 billion by the end of this year. Well, when we are talking about the growth of big data analytics certification, then it's not just the use of this technology which is transforming the world, rather it is also paving the way for a lot of development of other technologies like machine learning, AI and deep learning. It would not be wrong to claim here that big data is also paving the way for a new set of job opportunities. It has led to the rise in the demand for data analytics certification programs.
Let's shift our focus back to deep learning with big data analytics and how it is developing under the quasi-open set:Well, if you are from the field of technology, then you would be aware of the concept of big data and deep learning. Now let's have a look at how these two are impacting each other.
Deep learning is a subset of machine learning. And these technologies work on data. The data is the key driver, and with the help of the right kind of algorithm, the data is interpreted, and conclusions are drawn to enable the machines to perform a task. When it comes to deep learning, then it finds its use cases across different segments like :
- Healthcare
- Manufacturing
- Finance
All these segments rely on data for the vast majority of their work. With the help of deep learning, we can expect the system to work flawlessly, which eventually makes it more efficient.
Here it becomes important to mention about the neural network that is one of the key algorithms working behind the scene of deep learning. Neural networks are designed to work like the human brain. They can analyze the data similar to that of a human brain and process it so that the machine can make a decision and perform accordingly. These are considered to be most viable for big data analytic because of their lower complexity and lower computational cost.
Providing quasi-open set to analyze the working of big data :Now shifting our focus on the quasi-open set, then here we try to create an environment where big data performs under this system, which is created by the developers.
Usually, the big data classification models work under a semi-supervised learning framework. This is the primary because of the available unlabeled sample and high cost to collect the labeled sample. Hence, it assumed that the unlabeled samples that we have are from both the same and different classes that are there in the labeled training samples, which is also called here as a quasi-open set. In simple words, the quasi-open set has samples from source classes, which are indicated by labeled and unlabelled training samples and from novel classes. What we can conclude here is that end-to-end learning is considered to be the most pliable solution for big data analytics only if models are trained to classify source classes and novel classes.
Well, this was one of the tests performed by developers to ensure that how does big data and deep learning work in this kind of environment. Big data experts continuously work in newer kinds of environments to check how the machine performs and what can enhance its efficacy without any flaw. The aim is to create a system that is flawless and performs just like a human.