04 Nov 2022
Role And Impact Of Deep Learning In Data Science
Works at Ekeeda
If you find a world in which deep learning might take over from commonly used data science techniques, it won’t be a surprise. Yes! In this world, technology is still under development, and it's not just algorithms that learn – the entire system does! The field is still in its infancy and growing, the underlying maths could be challenging and complex to understand. Still, prominent data scientists can run their programs on neural networks without much worry about their work. The blog will help us understand Deep Learning and how to bring deep learning into practice in the field of data science.
Simply put across, Deep learning is a subset of ML that has been proven successful in helping recognize data patterns. It’s termed as a neural network-based approach that will allow computers to learn to do things independently rather than being programmed by humans. It has been successfully used in varied applications such as Biometrics, Facial recognition, translating language, and predicting financial outcomes.
Experts forecast deep learning to become the dominant technique for data analysis in the coming few years, and its impact on data science will be significant. It’s simple – Deep Learning algorithms can learn more from data than traditional ML analytics and algorithms, That’s because they can learn not only from data input but also from the hidden layers of data that will present higher-level concepts. It means Deep Learning algorithms will be used to automatically discover data patterns that are difficult or impossible for humans to discover.
In addition, deep learning algorithms would be trained on massive datasets, which gives them an advantage over traditional ML algorithms which are struggling with big data. As a result of these benefits, Deep learning is having significant impacts on the fields like computer science, AI, Robotics, computer vision, and Natural Language Processing (NLP). It is likely to become the dominant data analysis technique across all domains in the near future.
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Data Science is the process of converting raw data into actionable insights that will be used to improve business operations, products, or services. It’s a combination of statistics, ML, and programming that will allow businesses to come up with a sense of the raw but potential data. Data scientists use algorithms to identify the hidden patterns amongst large datasets and then use these insights to make better and data-driven decisions. There are various types of data scientists who fall under two important categories – predictive analytics and prescriptive analysis.
While Predictive analytics focuses on generating ML predictions about future events from a dataset, prescriptive analytics will identify potential opportunities and optimize decision-making processes by giving recommendations based on data analysis. In contrast, perspective analytics focuses on proactively creating suggestions to improve performance in a given area based on previous data. This approach is often used in data-driven companies to help improve business processes by predicting current & future trends as well as patterns.
Deep learning is a subset of Machine Learning that will allow computers to learn without being deeply programmed. It will enable computers to adapt and improve independently with repeated data exposure. It works by training a deep neural network on large data amounts, allowing computers to learn how to recognize data patterns. It can be used in various ways such as image recognition and NLP – Natural Language Processing. Deep Learning has been increasingly applied to data science, as it offers a more complex & flexible way to handle data than traditional ML methods. It can be used to process significant data amounts in fast, thereby way making it a very essential tool for researchers in the data field.
Deep Learning has brought a revolution in various ways - such as computer vision, NLP – Natural Language Processing, and ML. Data Science uses deep learning optimally to identify patterns and trends in BIG datasets. Some of the popular applications of deep learning in data science include:
Image Classification - Deep Learning is used to classify images automatically into various categories. For instance, the deep learning algorithm is trained to identify objects or facial expressions in the images.
Object Detection - Object detection in pictures or videos could be done through deep learning. For instance, the deep learning algorithm is used to detect people walking around in driving applications.
Generate Text - Deep Learning is used to generate text based on the given input. For instance, deep learning is used to create descriptions of products based on product images.
Watch: Deep Learning For Data Science
There are several benefits of Deep Learning in Data Science such as:
Better Efficiency – Yes! In deep learning, data scientists can gain a high level of accuracy and speed – which is essential for complex tasks like predicting trends or answering questions.
Deeper Insights – Deep learning let you detect patterns & insights that would otherwise be hidden in data. It’s because deep learning algorithms can learn and extract features from data automatically. You don’t have to manually specify the features to look for – the algorithm figures it out for you.
More Flexibility – It allows you to adapt your model to suit your requirements rather than follow a predefined set of rules. It increases flexibility to lead to accurate predictions and more informed decisions.
Improved Collaboration – Deep learning allows data scientists to collaborate in a more effective way by sharing models and insights. It helps them better understand the data and increases their chances of better data analysis.
Solve Complex Problems – Deep learning can solve complex and brainstorming problems. For instance, deep learning can classify images into various categories like Lions or Leopards. The task is quite tedious and time-consuming, but deep learning can achieve better and more efficient results in a quick time rather than traditional ML techniques.
Study Complex Data – Deep learning has the specialty to learning intricate patterns & data structures that would be difficult for other algorithms to figure out. That’s because deep learning algorithms can learn the hierarchy level and with learning it sorts out complex patterns. Thus, data scientists are able to see through the ways that would be easy and seem difficult for other algorithms to be able to detect.
On A Concluding Note –
In this world data is crucial and it’s been floating around in quintillions across the globe. It’s important to get the meaning out of it else it will prove useless for businesses. Whether to understand customer behaviour or predict future events, data science is the new epicenter for things to be carried out. Along with this, it's important to learn new techniques as well and one such thing is deep learning. What exactly is deep learning and why are these techniques so important in data science? This blog will give you a brief over of deep learning and its various applications in the data science field. We hope this will help you understand the importance of deep learning in data science & why you should incorporate it into your work as a data scientist.
Data is a vital asset for any form of business, and if you do not make efficient use of it – it will be a waste collecting it. Advanced-Data science technique like deep learning turns raw data into meaningful insights and presents it to clients in an attractive way, which can be used to make various crucial business decisions. Deep learning is an in-depth technique to process data in a more sorted and simplified way. It’s taking the data science world by its storm. Data science contributes to nearly every industry because each business in today’s era highly depends on data so it's important to learn data science.
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