Applications of Deep Learning
Deep Learning has revolutionized the world of Artificial Intelligence by creating machines that can solve problems and learn concepts beyond human intelligence. In today’s fast-paced world, businesses are increasingly using Deep Learning to their advantage, such as predicting consumer behavior, detecting market trends, and generating marketing strategies. The global market value for Deep Learning is projected to reach $44.3 billion by 2027. In this article, we’ll examine some of the top applications of Deep Learning, including Healthcare, Personalized Marketing, Financial Fraud Detection, Natural Language Processing, Autonomous Vehicles, Fake News Detection, Facial Recognition, Recommendation Systems, Smart Agriculture, and Space Travel. Let’s first understand what Deep Learning is before we dive into these applications.
Deep Learning: Understanding Artificial Neural Networks
Deep learning is a machine learning subdiscipline that utilizes artificial neural networks (ANNs) for computational operations. ANNs consist of nodes or neurons that receive input, compute it, and pass the output to the next layer until it reaches the last layer. In a deep learning system, all layers except for the input and output layers stay hidden and are referred to as hidden layers. ANNs are either biological or artificial, and the latter finds use in various AI applications.
Top 10 Applications of Deep Learning
In the previous section, we covered the fundamentals of Deep Learning. Let’s dive into the most popular applications of Deep Learning in AI.
Healthcare Sector and Deep Learning
Modern technology has revolutionized the healthcare sector, and Deep Learning is no exception. Deep Learning has found a variety of applications in the medical industry. These include:
– Interpreting medical data for disease diagnosis, prognosis, and treatment
– Personalizing treatments
– Monitoring patient health
– Analyzing medical imaging such as CT scans, MRIs, X-rays, etc.
– Drug prescriptions and more.
One notable application of Deep Learning is cancer diagnosis and treatment. Medical professionals use Convolutional Neural Networks (CNN) to grade different types of cancer cells. High-resolution histopathological images are exposed to deep CNN models magnified either 20X or 40X. The deep CNN models demarcate various cellular features within the sample and detect carcinogenic elements. For insight, please read [https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-021-00968-x].
Personalized Marketing with Deep Learning
Personalized marketing has become increasingly popular in recent years with marketers targeting individual’s pain points and offering exactly what they need. Deep Learning plays a significant role in this process.
Consumers today generate vast amounts of data through social media platforms, IoT devices, and the internet. However, this data may not be consistent or uniform in format, such as text, audio, video, etc.
Businesses use customisable Deep Learning models to interpret data from different sources and distil valuable customer insights. They leverage this information to predict consumer behavior and efficiently target their marketing approach. This is how online shopping sites recommend items tailored to individual preferences.
Financial Fraud Detection using Deep Learning
Financial corporations such as banks and insurance firms are most vulnerable to fraudulent transactions. Deep Learning can help these organizations in detecting and predicting financial fraud. Anomaly detection using deep learning algorithms like logistic regression, decision trees, and random forest can be used to analyze patterns common in valid transactions. With these models, potentially fraudulent financial transactions can be flagged. Fraud detection can deter various types of fraud such as identity theft, insurance fraud, investment fraud, and fund misappropriation.
Natural Language Processing with Deep Learning
Deep Learning is producing encouraging results in the field of Natural Language Processing (NLP). NLP focuses on enabling machines to comprehend human language, which is complex, and it involves context, writing styles, and accents, making it tricky for machines to interpret.
Deep Learning-based NLP resolves these complexities by training machines using Autoencoders and Distributed Representations. For instance, personal assistants embedded in smartphones use Deep Learning-based NLP to understand human speech and respond appropriately. Additionally, Deep Learning-based NLP can translate websites written in one language to a user-specified language.
The idea of self-driving cars began 45 years ago when a semi-automatic car with cameras and an analogue computer was first unveiled. It wasn’t until 1989 that deep learning was utilized through neural networks with ALVINN. Today, autonomous vehicles use LiDARs, motion sensors, and geo-mapping to navigate and gather information, which is then fed to deep learning algorithms to execute tasks such as accelerating, steering, braking, detecting pedestrians and other vehicles, and recognizing traffic signs. Self-driving vehicles powered by deep learning will soon become the majority of road traffic, making roads safer and more efficient.
Fighting Fake News with Deep Learning
Fake news has become a big problem, especially with the rise of social media. It can be used to misinform people, influence political campaigns, and harm individuals or situations. To stop this, we need to find a way to detect fake news. Deep Learning can help us achieve this by using complex language detection techniques to classify sources of fraudulent news. This technique works by comparing the information against trusty sources and verifying its authenticity. The combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) can accomplish this with high accuracy as described in this paper: [https://www.sciencedirect.com/science/article/pii/S2667096820300070].
Facial Recognition is a biometric technology that identifies individuals from images and videos by capturing their faces. It became prevalent with the integration of neural networks in the 1960s, which has improved accuracy tremendously.
Deep Learning powered Facial Recognition works by taking face embeddings and using them in a trained model to match images with a vast database. For example, DeepFace uses a nine-layer neural network to identify individuals with a 97% accuracy rate using around four million images of approximately 4,000 people.
Have you ever wondered how Spotify figures out which genres you like or how Netflix gives you personalized recommendations? The answer is Deep Learning – a machine learning technique that can identify underlying relationships in vast amounts of data.
Deep Learning models collect and analyze user data from various sources to extract relevant information. This information is then used by recommender systems to generate personalized suggestions for users.
While audio and video streaming services use this technology extensively, social media networks also utilize similar systems to recommend relevant posts, accounts, videos, and other content to users in their feeds.
The use of Artificial Intelligence and its subsets, including deep learning, has permeated numerous industries, including agriculture. Smart farming has emerged as a means to enhance traditional agriculture methods by leveraging IoT devices, GPS, remote sensing, satellite-based soil-composition detection, and other technologies.
Deep Learning algorithms are used to capture and analyse agriculture data to improve crop and soil health, predict weather patterns, and detect diseases. In crop genomics, experts use neural networks to determine the genetic makeup of various crop plants, thereby increasing their resilience to natural phenomena and diseases, increasing crop yield per unit area, and breeding high-quality hybrids.
// Code sample for implementing smart farming using deep learning
// (some parts are pseudocode and for illustration purposes only)
soil_composition_analysis = deep_learning_library.run_soil_composition_detection()
weather_prediction = deep_learning_library.run_weather_prediction_model()
crop_disease_detection = deep_learning_library.run_disease_detection_model()
if soil_composition_analysis indicates low nitrogen levels:
suggest suitable fertilizers
if weather_prediction indicates a low likelihood of rain:
recommend irrigating crops
if crop_disease_detection detects signs of disease:
recommend treating the crop with appropriate methods
Space Travel – AI and Deep Learning in Astronomy
Space travel is often associated with advanced technology such as humanoid robots, hyper-intelligent AIs, hi-tech equipment, etc. This technologically demanding field requires the latest and most efficient technologies to ensure safety, integrity, and success. AI, Machine Learning and Deep Learning play a crucial role in astronomy and space missions.
Deep Learning is used in automating the landing of rockets and building space flight systems that can make intelligent decisions without human intervention. It is also crucial in helping future Mars rovers to navigate and deduce their surroundings more independently.
Top 10 Deep Learning Applications
Here are 10 amazing deep learning applications that provide a glimpse of its potential to transform industries and organizations.
While these examples only scratch the surface of what deep learning can achieve, they hint at the exciting possibilities that lie in store.
In the years to come, we can expect deep learning to empower everything from advanced personal assistants to autonomous vehicles, forever changing the way we live and work.
For those interested in deep learning, here are some helpful resources:
Check them out to further your knowledge in this field!