Deep Learning is a subdivision of Machine Learning and can be put to use in areas where complex problems needs solutions and for building intelligent solutions. The deep learning technique trains the computer to learn with the help of examples which is similar to how a human brain learns. As a result, accurate decisions or predictions can be made based on data.
More about Deep Learning
Deep learning is the process by which a computer model learns how to execute categorization tasks directly from images, text, or sound. These models can even surpass humans with their accurate predictions. Deep Learning models are trained to utilize a large set of labeled data and neural network architectures with many layers.
The technology behind autonomous vehicles, voice assistants such as Alexa, Siri, and Google assistant is Deep Learning which couldn’t even be imagined as a possibility previously. However, currently, almost everyone is aware of these. Deep Learning is almost used in all businesses and below are some applications where Deep Learning is at play.
- Autonomous Vehicles
As discussed earlier, the technology behind autonomous vehicles without a doubt is Deep Learning. A system is fed a million sets of data in order to create a model, train the machines to learn, and then test the results in a secure environment. The Uber Artificial Intelligence Labs in Pittsburg is working not only to make driverless cars more common but also to integrate several smart features, like food delivery options, with the use of driverless cars.
One major issue encountered by autonomous vehicle developers is the cropping up of unexpected scenarios. With increasing exposure to millions of scenarios, a regular cycle of testing and implementation typical of deep learning algorithms ensures safe driving. Data from cameras, sensors, and geo-mapping is assisting in the development of concise and sophisticated models for navigating traffic, identifying paths, signage, pedestrian-only routes, and real-time elements such as traffic volume and road blockages.
According to Forbes, MIT is working on a new system that will allow self-driving cars to navigate without a map, as 3-D mapping is still limited to high-traffic areas and is less effective at avoiding mishaps.
- News Aggregation and Detection of False News
There is now a way to remove all of the negative and offensive news from your news feed. The extensive use of deep learning in news aggregation is bolstering efforts to tailor news to individual readers. While this may not appear to be novel, newer levels of sophistication in defining reader personas are being met in order to filter out news based on geographical, social, and economic parameters, as well as the individual preferences of a reader. In today’s world, where the internet has become the primary source of all genuine and fake information, fraud news detection is a valuable asset. As bots replicate fake news across channels, it becomes increasingly difficult to distinguish it.
The Cambridge Analytica scandal is a textbook example of how fake news, personal information, and statistics can be used to influence reader perception (Bhartiya Janata Party vs Indian National Congress), elections (Read Donald Trump Digital Campaigns), and personal data (Facebook data for approximately 87 million people was compromised).
Deep Learning aids in the development of classifiers that can detect fake or biased news and remove it from your feed while also alerting you to potential privacy breaches. It is extremely difficult to train and validate a deep learning neural network for news detection because the data is riddled with opinions and no one party can ever decide whether the news is neutral or biased.
- Robotics
Deep Learning is widely used in the development of robots that can perform human-like tasks. Deep Learning-powered robots use real-time updates to detect obstacles in their path and instantly plan their journey. It can be used to transport goods in hospitals, factories, warehouses, inventory management, product manufacturing, and so on.
- Natural Language Processing
Understanding the complexities of language, whether syntax, semantics, tonal nuances, expressions, or even sarcasm, is one of the most difficult tasks for humans to master. Constant training from birth and exposure to various social settings assist humans in developing appropriate responses and a personalized form of expression to each scenario. Deep Learning Natural Language Processing is attempting to achieve the same goal by training machines to detect linguistic nuances and frame appropriate responses.
- Captioning of Images
Image captioning is a technique for creating a textual description of an image. It employs computer vision to comprehend the image’s content and a language model to convert the comprehension of the image into words in the correct order. To convert the labels into a coherent sentence, a recurrent neural network such as an LSTM is used. Microsoft has created a caption bot in which you can upload an image or the URL of any image and it will display the image’s textual description. Caption AI is another app that suggests a perfect caption and best hashtags for a photo.
- Virtual Assistants
Virtual Assistants are cloud-based applications that recognize natural language voice commands and perform tasks on the user’s behalf. Virtual assistants like Alexa, Cortana, Siri, and Google Assistant are some of the best examples. To fully utilize their capabilities, they require internet-connected devices. Each time a command is given to the assistant, Deep Learning algorithms tend to provide a better user experience based on previous experiences.
- Image Coloring
Deep Learning has made significant advances in image colorization. Image colorization is the process of taking a grayscale image as input and producing a colorized image as output. A picture colorization model is illustrated by ChromaGAN.
An adversarial model frames a generative network that learns to colorize by blending an emotional and semantic understanding of both class distributions and color.
- Chatbots
Chatbots can solve customer issues in a matter of seconds. A chatbot is an AI application that allows you to chat online using text or text-to-speech. It can communicate and perform actions in the same way that humans do. Chatbots are widely used in customer service, social media marketing, and client instant messaging. It responds to user inputs with automated responses. It generates various types of reactions using machine learning and deep learning algorithms.
- Entertainment
Companies like Netflix, Amazon, YouTube, and Spotify provide relevant movie, song, and video recommendations to their customers to improve their experience. Deep Learning is responsible for all of this. Online streaming companies make product and service recommendations based on a person’s browsing history, interests, and behavior. Deep learning techniques are also used to automatically generate subtitles and add sound to silent movies.
- Healthcare
Deep Learning has found use in the healthcare industry. Deep Learning has enabled computer-aided disease detection and computer-aided diagnosis. Through the process of medical imaging, it is widely used for medical research, drug discovery, and the diagnosis of life-threatening diseases such as cancer and diabetic retinopathy.
- Advertising
Deep Learning in advertising allows for the optimization of a user’s experience. Deep Learning assists publishers and advertisers in increasing the importance of advertisements and boosting advertising campaigns. Ad networks will be able to cut costs by lowering the cost per acquisition of a campaign from $60 to $30. Data-driven predictive advertising, real-time ad bidding, and target display advertising are all possibilities.
- Automatic Handwriting Generation
This Deep Learning application involves the creation of a new set of handwriting for a given corpus of a word or phrase. The handwriting is essentially provided as a series of coordinates used by a pen to create the samples. The relationship between pen movement and letter formation is discovered, and new examples are generated.
- Pixel Recovery
Until Deep Learning came into play, the idea of zooming into videos beyond their actual resolution was unthinkable. Researchers at Google Brain trained a Deep Learning network in 2017 to take very low-resolution images of faces and predict the person’s face from them. The Pixel Recursive Super Resolution was the name given to this method. It significantly improves photo resolution, highlighting prominent features just enough for personality identification.
Conclusion
Deep Learning has gained prominence in almost every business sector. It is used in many industries, including e-commerce, healthcare, advertising, manufacturing, and entertainment. Deep Learning has made things possible such as autonomous cars, and voice assistants which couldn’t even be considered possible or imagined before this technology came into existence, and also making life simple for everyone.
