AI ML

What is Deep Learning? Models, Applications, and Examples

Written by John Terra
|
Updated on February 13, 2025

The human brain is often considered the ultimate computer, easily outpacing even our best artificial intelligence. This is why machine learning engineers try to emulate it to make a better machine. Enter deep learning.

This article explores and explains deep learning. It also covers the various deep learning applications, use cases, models, pros and cons, and more. Additionally, we will compare machine learning vs deep learning. Finally, we’ll talk about an online AI ML bootcamp for those ready to get started in this field.

So, let’s dive in by answering the question, “What is deep learning?”

What is Deep Learning? The Basics

Deep learning is considered a machine learning subset that utilizes multilayered neural networks, called deep neural networks, to imitate the complex decision-making power of human brains. It is based on artificial neural network architecture. An artificial neural network (ANN for short) employs layers of interconnected nodes known as neurons that work together to process and learn from input data.

Our organic human brains inspire artificial neural networks. They can solve numerous problems using techniques like natural language processing, image recognition, and speech recognition.

Also Read: Machine Learning Engineer Salary: Expected Trends in 2025

How Does Deep Learning Work?

Neural networks, or artificial neural networks, try to imitate the human brain via a mix of data inputs, bias, and weights, all acting like silicon neurons. These elements work in concert to accurately recognize, classify, and describe objects found in the data.

Deep neural networks consist of multiple layers of interconnected nodes, each building on its predecessor to refine and optimize categorization or prediction. This computation progression through the network is known as forward propagation. The deep neural network’s input and output layers are called visible layers. The input layer ingests the data for processing, and the output layer makes the final prediction or classification.

Another process, called backpropagation, uses algorithms like gradient descent to calculate prediction errors. It then adjusts the function’s weights and biases by moving backward through the layers, thus training the model. When used together, forward and backpropagation let a neural network make predictions and correct errors. Over time, the algorithm gradually becomes more accurate.

Deep learning needs a vast amount of computing power. Consequently, high-performance graphical processing units (GPUs) are best because they can handle extensive calculations in multiple cores, each with plenty of available memory. Managing multiple GPUs in-house can strain internal resources significantly and be highly costly to scale. Most deep learning apps are coded using the JAX, PyTorch, or TensorFlow learning frameworks.

Types of Deep Learning Models

Deep learning models can automatically learn features from data. This makes them ideal for speech and image recognition and natural language processing tasks. Convolutional neural networks (CNNs), feedforward neural networks, and recurrent neural networks (RNNs) are often used in deep learning architectures.

Convolutional neural networks are designed particularly for image and video recognition tasks. CNNs can automatically learn features from images, making them well-suited for object detection, image classification, and image segmentation tasks.
Feedforward neural networks are the most basic type of ANN, employing a linear information flow through the network. FNNs are extensively used for speech recognition, image classification, and natural language processing.
Recurrent Neural Networks process sequential data, such as natural language and time series. RNNs can maintain an internal state that gathers information about previous inputs, making them ideal for language translation, speech recognition, and natural language processing tasks.

Also Read: How to Become a Robotics Engineer in 2025? A Comprehensive Guide

Machine Learning vs. Deep Learning

Machine learning and deep learning are offshoots of artificial intelligence. However, machine learning is a broader term encompassing various techniques, including deep learning. Depending on the algorithm and task, both algorithms can be trained using labeled or unlabeled data.

Engineers rely on machine learning and deep learning for jobs such as natural language processing (NLP) and image or speech recognition. However, deep learning usually outpaces traditional machine learning in challenging and complex pattern recognition tasks such as image classification and object detection, thanks to the former’s ability to learn hierarchical data representations.

Additionally, machine learning takes less time to train models, can work on smaller datasets, is less complex overall, and requires less computing power than deep learning.

Deep Learning Use Cases and Applications

Application Modernization

Generative AI can enhance developers’ capabilities and reduce the ever-increasing skills gap in the application modernization and IT automation domains. Thanks to recent breakthroughs, generative AI for coding is possible in natural language processing (NLP) and large language model (LLM) technologies. It employs deep learning algorithms and large neural networks, which are trained on massive datasets of existing source code.

Programmers can enter plain text prompts that describe what the code should do. Generative AI tools recommend code snippets or full functions. This streamlines the coding process by tackling repetitive, boring tasks and reducing the need for manual coding.

Computer Vision

Computer vision is a subdivision of artificial intelligence (AI) that covers object detection, image classification, and semantic segmentation. It leverages neural networks and machine learning to teach computers and learning systems to extract relevant information from videos, images, and other visual sources and to make recommendations and take action when the system finds defects or other problems. If AI helps computers think, computer vision helps them see, observe, and comprehend.

Since computer vision systems are often trained to monitor production assets or inspect products, they can analyze thousands of products or processes per minute, noting otherwise imperceptible defects or issues. Computer vision is found in industries ranging from utilities and energy to manufacturing and automobiles.

Computer vision requires plenty of data. It repeatedly runs analyses until it ultimately recognizes images. Computer vision uses algorithmic models to allow a computer to teach itself about visual data context. If sufficient data gets fed through the model, the computer can then “look” at the data and teach itself to discern one image from another. Algorithms empower the machine to learn by itself, instead of relying on a programmer to make it recognize an image.

Also Read: Today’s Top 10 AI Technologies: Here’s Everything You Should Know

Some common computer vision applications can be seen in these industries:

Automotive: Although the driverless car age hasn’t exactly arrived, the underlying technology is making inroads into automobile manufacturing, improving driver and passenger safety with features like lane line detection and automatic braking systems.
Healthcare: Computer vision has become part of radiology technology, helping doctors better identify cancerous tumors.
Social Media Marketing: Social media platforms offer suggestions on the identity of a photograph posted on a user profile, making it easier to tag friends.
Retail: Some e-commerce platforms now use visual search, which allows brands to recommend items that could complement a customer’s wardrobe.

Customer Care

AI is helping businesses better understand and accommodate increasing consumer demands. Generative AI can further unlock a host of benefits for direct-to-consumer models, highly personalized online shopping, and delivery services. These benefits can improve customer care, talent transformation, and application performance.

AI allows businesses to adopt a customer-centric approach by utilizing valuable insights from customer feedback and their buying habits. This data-driven approach helps improve product design and packaging, increasing customer satisfaction and boosting sales.

Gen AI can also function as a cognitive assistant for customer care, offering contextual guidance based on past conversation history, sentiment analysis, and even call center transcripts. Generative AI can also enable personalized shopping experiences, create and grow customer loyalty, and offer the business a competitive advantage.

Digital Labor

Organizations can supplement their workforce by constructing and deploying robotic process automation (called RPA) and digital labor to collaborate with humans. These processes increase productivity and provide help whenever backup is needed. For example, digital labor can help developers update legacy software faster.

Digital labor employs foundation models to automate and improve knowledge workers’ productivity by enabling self-service automation in a quick, reliable way. Rather than having IT experts record and encode repetitive action flows, knowledge workers can use digital labor automation for self-service automation using a foundation of model-powered conversational instructions and demonstrations. For instance, no-code digital apprentices can help end-users who don’t have programming expertise speed up app creation by teaching, supervising, and validating code effectively.

Generative AI

Generative AI (also known as Gen AI) is an AI category that autonomously generates images, video, text, data, and other content in response to the user’s request or prompt. It relies on deep learning models that learn from patterns found in existing content and generate new, similar content based on its training. Gen AI has applications in many fields, including marketing, customer service, software development, and research.

Generative AI is ideal for handling diverse data sources like emails, videos, images, audio files, and social media content. These forms of unstructured data create the backbone for developing models and ongoing generative AI training so it can constantly remain effective. This unstructured data can be used to improve customer service through chat bots or facilitate more effective email routing.

Despite its much-discussed risks and limitations, many companies are going all-in on Gen AI, discovering how their organizations can harness generative AI to improve internal workflows, enhance products and services, and cut costs. The big question is, how do we make the workplace more efficient without creating ethical or legal issues?

Natural Language Processing and Speech Recognition

NLP combines the rule-based modeling of human language with statistical and machine learning models to allow computers and other digital devices to recognize, comprehend, and produce text and speech. NLP powers devices and applications that can respond to spoken or typed commands, translate text into different languages and recognize and authenticate users based on voice.

Statistical NLP is a subset of NLP that combines computer algorithms with deep learning and machine learning models. This approach helps automatically extract, classify, and label text and voice data elements and assign statistical likelihoods to each element’s possible meaning. Today, RNN-based deep learning models and techniques enable NLP systems to “learn” as they work, extracting even more accurate meanings from vast volumes of raw, unstructured, and unlabeled voice and text datasets.

Speech recognition, alternately called automatic speech recognition (ASR), computer speech recognition, or speech-to-text, processes human speech into a written format. Although speech recognition is often confused with voice recognition, speech recognition is dedicated to translating speech from a verbal to a text format. In contrast, voice recognition simply identifies a user’s voice.

Also Read: Top 40 Machine Learning Interview Questions & Answers

Advantages of Deep Learning

There are many advantages to employing deep learning models, like:

Automated feature engineering. Deep learning algorithms can automatically find and learn relevant features from data without needing manual features.
Continual improvement. Deep learning models continually improve their performance as they access more data.
Flexibility. People can apply deep learning models to a diverse range of tasks and work with many different data types, including images, text, and speech.
High accuracy. Deep learning algorithms can achieve state-of-the-art performance when executing different tasks like natural language processing and image recognition.
Scalability. Deep learning models can scale to accommodate large, complex datasets and learn from vast amounts of data.

Disadvantages of Deep Learning

Deep learning also has its share of challenges, such as:

Its black-box nature. People often consider deep learning models black boxes, making understanding how they work and arriving at their predictions challenging.
There are high computational requirements. Deep learning AI models need vast quantities of data and computational resources.
Models need lots of labeled data. Deep learning models usually require plenty of labeled data for training. Acquiring this data is potentially expensive and time-consuming.
Overfitting. Deep learning models run the risk of overfitting to the training data. Overfitting happens when a deep learning model is trained to fit excessively close to the training data, translating into poor performance on new, unseen data.
Interpretability challenges. Deep learning models can sometimes be challenging to interpret, making it difficult for people to understand how the models make decisions.

Also Read: Applications of AI in Business: Transforming Ideas into Impact

Deep Learning Careers

Deep learning offers many employment opportunities, including:

Associate Data Scientist
Data Scientist
Machine Learning Research Engineer
Machine Learning Engineer
Machine Learning Product Manager
Big Data Engineer
Computer Vision Engineer
NLP Engineer
AI/ML Research Scientist

Do You Want to Learn More About Machine Learning?

If you’d like to learn more about this revolutionary new technology and maybe even begin a new career, check out this online AI ML program. This comprehensive program teaches you ML, NLP, and Python via a high-engagement learning experience.

Indeed.com shows that deep learning engineers earn an average salary of $155,848. So, explore this AI ML course and start an exciting new career in one of today’s hottest technologies.