Exploring Deep Learning Examples

Deep learning (DL) is a branch of artificial intelligence and machine learning (ML) that uses layers of neural networks to help machines “learn” in ways remarkably parallel to the function of the human brain. It allows machines to recognize patterns, solve problems, and make choices in ways that mimic how humans learn from experience. 

This technology powers some of today’s innovative tools. If you’ve ever used a virtual assistant like Amazon Alexa or Google Home or unlocked your smartphone using the image of your face, you have already experienced deep learning in action. Professionals use deep learning technology in various real-world applications, from chatbots to image recognition. 

Continue reading to discover real-world applications of deep learning, which can help you better understand the technology and how it changes the applications you use in everyday life and beyond.

Understanding deep learning

To understand deep learning, you must first grasp how it differs from traditional machine learning. Deep learning uses layered neural networks to analyze data, which allows machines to learn and improve over time. The computational layers can extend to hundreds of thousands of levels, hence the term “deep” learning. These layers extract characteristics and relationships from raw data, which they continuously learn from and improve.

In contrast, machine learning (ML) is more straightforward, typically involving up to two computational layers that form the neural network architecture. This simplicity requires more human intervention to rectify mistakes, manually input data, check and improve algorithms, and identify features. 

What is a neural network?

These programs or models contain layers of artificial neurons called “nodes,” with an input layer, output layer, and one or more hidden layers. The nodes are all connected. Once activated, the node (or nodes) transmit data to the next layer in the network in a process that mirrors the biological activity in the human brain.

Real-world applications of deep learning 

Deep learning's versatility means you can apply it in numerous fields, using its power to solve complex problems and increase efficiency. Deep learning helps drive innovations across multiple sectors. Deep learning examples include:

• Image and speech recognition: Deep learning algorithms are integral to facial recognition systems used for security purposes, including unlocking your smartphone. An everyday example is facial recognition on Facebook (Meta) for automatic tagging. It is also used in voice-activated assistants, such as Siri, Alexa, and Google Assistant, for speech recognition and natural language understanding.

• Autonomous vehicles: Self-driving cars use deep learning algorithms to process data from vehicle sensors and cameras in real-time, enabling them to recognize objects and traffic movements and make decisions to navigate roads safely.

• Natural Language Processing (NLP): Deep learning algorithms leverage computational linguistics and the rules of human language to help computer systems understand human speech. With NLP, computers and chatbots can perform various functions, including language translation and sentiment analysis, improving their ability to understand and respond to textual data more humanistically.

Deep learning examples that are revolutionizing industries

The impact of deep learning is much more than the single examples above. Advancements in deep learning are revolutionizing industries, bringing technology that is changing how people work. Some of the industries that benefit from deep learning include the following:

• Health care: In the medical field, you can use deep learning to ensure accurate diagnoses through precision medicine, which uses medical imaging analysis. It identifies patterns in patient data to preempt disease progression and aid in drug development by predicting molecular behavior. A prime example would be a radiology practice using computer vision to detect cancer tumors better. 

• Financial services: In the finance industry, deep learning can be used for algorithmic trading, fraud detection, and risk management of loans and investments. Deep learning models analyze market trends and customer data to make informed decisions and automate investing. 

• Manufacturing and supply chain: Deep learning helps in predictive maintenance. You can implement it to help identify potential equipment failures before they happen and optimize manufacturing processes by visually inspecting defects.

• Entertainment and media: Have you ever browsed Netflix and received recommendations for what to watch next? Many platforms, including Netflix and Spotify, leverage the power of deep learning to analyze users’ data and make personalized recommendations according to each user’s habits.

• Retail and e-commerce: Deep learning drives recommendation engines, improves customer service through chatbots and personalized shopping experiences, plus optimizes inventory management. 

• Agriculture: Deep learning uses precision farming techniques like monitoring crops and automating functions like harvesting and planting. It also uses AI to predict weather changes, water consumption, and general crop health, boosting production while helping save time and money.  

• Environmental protection: Regarding environmental concerns, deep learning assists in climate modeling, remote sensing, pollution level monitoring, and biodiversity tracking to aid conservation efforts.

How to get started with deep learning

Deep learning is a subset of machine learning with advanced capabilities to improve everyday functions through technology. If this exciting and emerging field interests you, consider the following structured approach to help you as you begin your deep learning journey.

1. Understand the basics

It’s best to begin with the basics as you start your deep learning journey. Build a solid foundation in math and develop your IT and programming skills. Learn data structures and create an understanding of algorithms while working on general coding and programming. Familiarize yourself with probability and statistics, which are fundamental to understanding artificial intelligence, machine learning, and deep learning algorithms.

Consider taking an online course such as Programming for Everybody, delivered by the University of Michigan.

2. Study machine learning 

As deep learning is a subset of artificial intelligence and machine learning, it’s essential to have a solid understanding of relevant concepts, algorithms, and problem-solving techniques. You may find online courses, engage in projects, and get to grips with machine learning tools. Pursuing a bachelor's degree in machine learning, computer science, information technology, or similar would be beneficial if you haven't done so yet.

3. Explore deep learning

As you move into deep learning, consider focusing on specialized learning. Delve into advanced topics, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). You might consider pursuing a master’s in machine learning or artificial intelligence.

4. Work on practical projects

Get hands-on experience by applying your knowledge and working on projects. For instance, you can utilize public data sets from sources like Kaggle and UCI Machine Learning Repository. You can also Google Dataset Search to practice and benchmark your models.

5. Join a community

Engage with the deep learning community in person by attending meetings or conferences or online via social media groups and forums. Doing so can provide support, inspiration, and opportunities for collaboration.

6. Continuous learning

The deep learning field continues evolving. Stay updated by following leading researchers, attending research conferences, and keeping informed about the latest advancements and tools via workshops and online courses.

Discover more about deep learning with Coursera

Deep learning is a dynamic field, with new applications and advancements emerging regularly. Remaining up-to-date on any changes and engaging with others can help support you as you discover ways to contribute to this exciting technology area and potentially shape its future. Consider enrolling in deep learning specialization courses on Coursera. Guided by experts in the field, these courses often include a series of interconnected classes that cover both theory and practical applications. For example, consider checking out the Machine Learning Specialization three-course program from DeepLearning.AI and Stanford University.