Advances in Data Science are transforming the industry by leaps and bounds, even as we speak. While the global AI market is projected to grow by nearly 54%, reaching USD 22.6 billion by 2021, the deep learning market is expected to grow at a CAGR of 41.7% to reach a market size of USD 18.16 billion by 2023.
These stats only prove that AI and deep learning are ruling the industry, penetrating almost every sector, including IT, healthcare, education, gaming, etc. The applications and use cases of ML and deep learning are both numerous and varied.
They’ve already changed how we interact with the world around us, how we go about our daily routine, and our consuming behavior. And needless to say, these new-age technologies will continue to change our surroundings and lives for years to come.
What is Deep Learning?
Deep learning is a subset of machine learning that aims to train machines via algorithms (neural networks) inspired and designed after the structure of the biological brain. The primary focus of deep learning is to teach machines what comes naturally to humans – to learn through examples and experience.
Deep learning uses neural network architectures comprising multiple hidden layers within, ranging from 2-3 to 150 layers. This is where the name “deep” learning comes from. Usually, large sets of data (labeled) data are used to train deep learning models and neural network architectures. This allows the models to learn and adapt to the features directly from the dataset instead of relying on manual feature extraction.
Deep learning trains computer models to perform classification tasks directly from text, image, or audio data. When trained adequately, deep learning models attain high-level accuracy, sometimes even outperforming human capabilities. It is deep learning technology that powers autonomous cars, voice recognition systems, virtual assistants, fraud detection systems, and natural language processing (NLP). Learn deep learning techniques.
Why Does Deep Learning Matter?
Deep learning is rapidly gaining popularity across industries because it promises unmatched levels of accuracy and efficiency. Although deep learning models are generally trained on labeled data, once trained, they become adept at “unsupervised learning,” meaning they can extract valuable insights from raw (unstructured or unlabeled) data.
This is precisely why deep learning is now being used in many areas – from making speedy and accurate medical diagnoses to enhancing personalization for eCommerce companies.
Here are a few applications of deep learning
Automated driving – Companies like Google and Tesla are experimenting with deep learning to improve automated driving. Powered by deep learning, self-driving cars can automatically detect objects like stop signs, traffic lights, vehicles, pedestrians, etc.
Industrial automation – Today, an increasing number of companies use deep learning technology to improve worker safety in manufacturing units, particularly around heavy machinery. Deep learning systems can automatically detect when workers/objects are unsafe and alert them, preventing unwanted accidents.
Aerospace – Aerospace organizations use deep learning to identify objects from satellites to locate areas of interest and identify safe/unsafe zones for troops and successful landing of spacecraft.
Medical research – Deep learning has extensive use cases in the field of medical research. For instance, in collaboration with NantWorks, researchers at UCLA developed a microscope powered by AI and deep learning to detect cancer cells within a few milliseconds – hundreds of times faster than any other method.
Virtual assistance – Deep learning is the technology behind speech translation and automated hearing. Smart personal assistants like Alexa and Siri are two of the best examples of deep learning applications for virtual assistance.
Visual recognition – Deep learning technology is used to develop state-of-the-art image recognition systems. These systems can classify and sort images according to multiple factors like location, dates, faces, objects, and events.
Fraud detection – The BFSI industry is leveraging deep learning to prevent and minimize fraudulent activities in the digital domain. Deep learning models are trained to identify customer transaction patterns, credit scores, and any abnormal behavior or outliers in their online transactions. Also, ML techniques like classification and regression are used for fraud detection.
Also Read: Deep Learning Prerequisites
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Benefits of Deep Learning
Machine’s process of mimicking the brain signifies a lot more benefits for humans in challenging digital endeavors. The creation of complex technology has already helped reap various benefits toward improved high-end innovations. Let’s find out more about the potential advantages of deep learning.
- Optimum data utilisation: Utilising unstructured data might be challenging for humans, but a fully automated device equipped with deep learning concepts will enable efficient data utilisation through a hoard of unstructured data.
- Cost reduction: Unnecessary costs implied due to minor human errors or spent on utilities can be saved by working with a device enriched with deep learning algorithms. Deep learning ensures optimum resource utilisation, evolving as a cost-effective method.
- Reduced need for feature engineering: Deep learning algorithms are capable of executing automatic feature engineering to promote faster and more efficient data operations.
- High-quality results: With the right training and algorithm, Deep learning can result in highly-efficient results that are cost-effective, accurate, consume less time, and ensure effective utilisation of data and resources.
Career Opportunities with Deep Learning
Future awaits expansion of businesses equipping deep learning methodologies to improve their business operations and enhance efficiency. In order to keep up with the growing demand, you must have the required deep learning certifications to proceed. Fortunately, you can learn through various NLP online course free of cost certifications offered under education portals like upGrad.
Let’s look at how you can use your NLP online course free of cost certifications through career opportunities in deep learning.
- Data Scientist: Data scientists have in-depth knowledge of data structures through deep learning algorithms that assess and cleanse heaps of unstructured data.
- Software Engineer: Software engineers create as well as take help from deep learning algorithms through their coding skills and implement the same to simplify tasks.
- Machine Learning Engineer: Machine learning engineers create complex algorithms behind deep learning operations and incorporate them into processes to make deep learning neural networks as strong as possible.
- Data Analyst: Deep learning is an imperative part of efficient data analytics that assists data analysts in structuring unstructured data mounts.
Learn NLP free: The upGrad advantage
Data Science and AI are two of the most happening fields in the industry right now, and if you wish to build a career in Data Science, gaining deep learning and NLP skills will prove beneficial for your professional career.
upGrad has a free deep learning course that’s perfect for Data Science aspirants. This free deep learning course is offered under the upStart-Priceless Learning initiative. It is a well-designed course that will teach you the fundamentals of deep learning and help build a strong foundation in NLP.
Taught by competent industry experts and mentors, you will learn about the different deep learning concepts, including neural networks, activation functions, data augmentation, etc. Mastering these concepts will help you to build innovative ML projects for solving real-world challenges. So why wait? Check out our NLP online course free.
The course is four weeks long and is divided into three modules
Maths for Data Analysis
This module focuses on teaching candidates about the essential mathematical tools and techniques required to understand deep learning and NLP concepts. You will also learn about NumPy syntax, NumPy arrays, and data cleaning and extraction via Python.
Intro to Deep Learning
The second module teaches the basics of neural networks (Feedforward and Backpropagation). Learners will also develop a deep learning project to identify handwritten digits.
Introduction to Natural Language Processing
The third module introduces learners to the basics of NLP. It also teaches candidates how to use regex to build tools for spell correction, phonetic hashing, spam detection, etc.
To conclude, deep learning is a valuable skill that can open up promising Data Science career opportunities. As more organizations start exploring the nuances of deep learning, new job roles will crop up in this field.
Must Read: Deep Learning Project Ideas
How to Start
To join our machine learning online course free, follow these simple steps:
- Head to our upStart page
- Choose the course you want to join
All the courses present on our upStart page are available for free and don’t require any monetary investment. These courses help you kickstart your learning journey and get acquainted with the fundamentals of such complicated subjects.
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If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.
Does online learning help in any way?
Since the pandemic started, we have experienced what online learning feels like. Sitting in front of the desktop for long hours is quite tiring, but there are many benefits to it as well. Online courses definitely help in reducing the costs and give you more flexibility in your time. You get to learn the things of your choice from the comfort of your home. The essential things required are commitment and discipline, without which you will not be able to take advantage of the course. Thus, to answer the question, online learning definitely helps if you have the right attitude and willingness to learn.
Is it possible to do well in deep learning without prior knowledge of coding?
Deep learning is basically a specialized field of machine learning. Deep learning engineers carry out deployment and modeling tasks. Since the conversion of codes is a mandatory responsibility of deep learning engineers, you should have prior knowledge of coding. It is not necessary to be an expert in all programming languages such as Python, C, or C++, but a solid understanding of the fundamental concepts is required.Generally, deep learning engineers are required to convert prototype codes into production codes, for which a fine knowledge of coding is a must.
How much time is needed to learn machine learning well?
Machine learning is an ever-evolving field. Looking at the enormous job openings in the machine learning field, more people are opting to learn it. If you are planning to learn machine learning from scratch, it may take you around 6–12 months. However, if you have prior knowledge of coding and some other concepts of machine learning, it may take a little less time than this. Various free online courses are available for you to learn machine learning effectively.