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Deep Learning: Dive into the World of Machine Learning!

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2nd Jun, 2023
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Deep Learning: Dive into the World of Machine Learning!

What comes to your mind when you hear the term “Deep Learning?” You probably think of smart robots and machines that will take over our world in the near future, right? Well, that’s not at all what deep learning is. In a layman’s term, deep learning is an AI approach that aims to imitate the workings of the human brain to process large amounts of data and extract meaningful patterns from it to foster data-driven decision making.

Today, data rules all – it is the new King of the digital world that we live in. Artificial Intelligence, Machine Learning, and Deep Learning are all focused on one thing – leveraging Big Data to power innovation. The interest in AI technology is soaring by the minute, and deep learning is the cutting-edge approach that is disrupting every industry. According to a recent research report by Tractica, the AI market is estimated to grow from 3.2 billion in 2016 to $89.8 billion by 2025. These figures only reinforce the fact that AI, ML, and Deep Learning will play an even bigger role in the development and transformation of the business and IT sector.

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What is Deep Learning?

Deep learning is deeply intertwined with Artificial Intelligence and Machine Learning. How, you ask?
As you can see, deep learning is a subset of ML which in turn is a subset of AI. Thus, while Artificial Intelligence is the broader umbrella that focuses on teaching machines how to think independently and intelligently, ML is an AI approach that aims to create such algorithms that can extract valuable information from large datasets. Deep Learning, on the other hand, is a branch of ML that uses a specific algorithm – Neural Nets – to achieve the end purpose of ML.

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What is Machine Learning and Why it matters

Deep learning is an exclusive technique for developing and training neural networks. The structure of a neural network draws inspiration from the structure of the human brain, more precisely, the cerebral cortex. Thus, similar to a cerebral cortex, an artificial neural network also has many layers of interconnected perceptrons. Unlike traditional data approaches that analyze data in the linear method, deep learning relies on the non-linear approach of training machines to process data. The data that is fed into the deep learning system passes through the interconnected network of hidden layers.
These hidden layers of the neural net process, analyze, modify, and manipulate the data to determine its relationship with the target variable. Each node of the net bears a specific weight, and every time the data passes through a node, it multiplies the input value by its weight. This process continues until it reaches the output layer, with the final output transforming into a valuable information. Deep learning, thus, eliminates the process of manual identification of patterns hidden in data.

Deep Neural Network Example – Image Credit: Texample

How does Deep Learning work?

Now that you have a deep learning introduction, let us understand its working. At its core, deep learning operates by using large amounts of labeled data and feeding it into neural networks. The neural networks then iteratively learn from this data by adjusting their internal parameters through a process known as backpropagation. This iterative learning process allows the networks to progressively improve their performance and accuracy over time.

Deep learning architectures are typically composed of an input layer, one or more hidden layers, and an output layer. Each layer consists of numerous artificial neurons that receive input, process it using weighted connections, and produce an output. The layers are densely connected, meaning that each neuron in one layer is connected to every neuron in the subsequent layer. This interconnectedness allows the network to capture complex relationships and dependencies within the data.

Career Opportunities in Deep Learning

Anyone in the IT world must have heard about deep learning at some point of his/her career. With AI progressing by leaps and bounds, the field of deep learning is also skyrocketing. Since deep learning is a rapidly growing field of research, it is creating massive job opportunities for individuals who specialize in AI and ML technologies. Today, the demand for skilled and trained professionals in deep learning, particularly for deep learning engineers and deep learning researchers, has increased by manifold across the various parallels of the industry.

According to a 2017 report by Grand View Research, Inc., the deep learning market in the US is projected to reach $10.2 billion by 2025.

Deep learning market revenues in the US (2014-25)

According to the latest stats on Indeed, the average salary for deep learning professionals in the US ranges anywhere between $71,935/year for a Deep Learning Research Scientist to $140,856/year for Deep Learning Computer Vision Engineer.  

Skills Required for a Successful Deep Learning Career

Since deep learning is a subset of ML, the skills required for deep learning are pretty much the same as required for ML. By now you’ve already guessed that programming knowledge is a must here. Most popular deep learning libraries are written in R and Python. Hence, if you are well-versed in any one of these two languages, it will suffice. Apart from possessing extensive knowledge of the fundamentals of Computer Science and programming, you must also have a solid foundation in Mathematics, Statistics & Probability, and Data Modeling.

A significant part of a deep learning engineer’s job is to design algorithms and systems that can seamlessly communicate with as well as integrate other software components that already exist. Thus, software design skills are a must in this field. You also need to be comfortable in working with standard ML libraries and algorithms including MLib, TensorFlow, and CNTK.

In-demand Machine Learning Skills

Deep Learning in the Real World

Deep learning has penetrated almost all the significant aspects of our lives. Whether we realize it or not, deep learning technologies are everywhere around us. Organizations and companies across the world are leveraging deep learning technology to power innovations like self-driving cars and chatbots to developing useful services like fraud prevention, predictive analytics, task automation, and much more.

Deep Learning Applications

Deep learning has found applications in various domains, revolutionizing industries and enabling breakthrough advancements. 

Now that you know what is deep learning in AI, explore some prominent applications of deep learning:

Computer Vision: Deep learning has significantly enhanced computer vision tasks, such as image classification, object detection, and facial recognition. By analyzing pixel-level data, deep learning models can accurately identify and classify objects within images and videos.

Natural Language Processing (NLP): Deep learning has greatly improved NLP tasks, including speech recognition, language translation, and sentiment analysis. Deep learning models can understand and generate human language, enabling advancements in virtual assistants, chatbots, and language-based applications.

Healthcare: Deep learning is making substantial contributions to the healthcare sector. It is being utilized for medical image analysis, disease diagnosis, drug discovery, and personalized treatment recommendations. Deep learning models can analyze medical images like X-rays, MRIs, and CT scans, assisting doctors in accurate diagnosis and treatment planning.

Autonomous Vehicles: Deep learning plays a vital role in enabling autonomous vehicles to perceive and understand their surroundings. Deep learning algorithms can process sensor data, such as images and LiDAR readings, to identify pedestrians, vehicles, and obstacles, allowing autonomous vehicles to navigate safely.

Importance of Deep Learning

The importance of deep learning lies in its ability to extract meaningful insights from complex and unstructured data. Traditional machine learning algorithms often struggle to handle high-dimensional data with intricate patterns. Deep learning, with its hierarchical representations and sophisticated neural networks, can capture and utilize these patterns effectively. This capability has opened up new possibilities in various fields, ranging from healthcare and finance to retail and entertainment.

Let us now look at some of the best use cases of deep learning in the real world!

    • One of the most excellent examples of deep learning tech is the personalized recommendation lists on online platforms such as Netflix, Amazon, and Facebook. The online and social media giants have access to a treasure trove of user-generated data. Using deep learning techniques, they are able to extract useful information from the user-generated data which is then used to create a customized and personalized list of suggestions for individual users according to their tastes and preferences.
    • Deep learning networks are capable of successfully analyzing behaviors in real-time. DeepGlint is a deep learning solution that can fetch real-time insights about the behavior of any object, be it humans or inanimate objects like cars.
    • Image recognition is another application of deep learning. Image recognition aims to recognize and identify objects within images while also understanding the content and context of the image. AlchemyAPI has been developing image recognition technology for quite a while now. CamFind is a mobile app that utilizes AlchemyVision API – it can not only inform the users about the objects in an image but can also tell them where they can purchase those objects from.
    • Deep learning applications have also found their way in the world of advertising. Ad networks and marketers leverage deep learning tech to build data-driven predictive advertising, targeted display advertising, and real-time bidding (RTB) advertising, to name a few. For instance, Baidu, a Chinese search engine uses deep learning to predict such advertising content and methods that the users can relate to. This helps increase the revenue of the company.
    • Pattern recognition powered by deep learning is being used by many companies to detect and prevent fraud. PayPal has been successful in preventing fraudulent payment transactions and purchases. It has achieved this with the help of H2O (an open-source predictive analytics platform) that uses advanced ML algorithms to analyze data in real-time to check for any anomalies that hint at fraudulent activities and security threats.
Artificial Intelligence Engineers: Myths vs. Realities

These are only a few use cases of deep learning from a vast pool of other innovative real-world projects. Deep learning, like AI and ML, is still emerging and developing. In the future, deep learning together with AI and ML will pave the way for more such groundbreaking innovations that’ll completely transform our lives in ways we cannot yet imagine.

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Deep Learning Limitations

Despite its impressive capabilities, deep learning does have certain limitations. One of the major challenges is the requirement for large amounts of labeled data for training. Deep learning models thrive on data, and obtaining a significant volume of high-quality labeled data can be time-consuming and expensive. Additionally, deep learning models are often considered “black boxes,” as their decision-making processes are not easily interpretable, raising concerns regarding transparency and accountability.

Difference Between Artificial Intelligence vs Machine Learning vs Deep Learning

Before diving deeper into deep learning, it’s important to understand the distinction between artificial intelligence (AI), machine learning, and deep learning. While AI encompasses the broader field of creating intelligent systems, machine learning is a subset of AI that focuses on algorithms that learn from data. Deep learning, on the other hand, is a specific approach within machine learning that utilizes neural networks with multiple layers.


Abhinav Rai

Blog Author
Abhinav is a Data Analyst at UpGrad. He's an experienced Data Analyst with a demonstrated history of working in the higher education industry. Strong information technology professional skilled in Python, R, and Machine Learning.
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