In this section you will learn the various applications of Deep Learning for development of deep neural network models to solve machine learning problems.In this section you will learn the various applications of Deep Learning for development of deep neural network models to solve machine learning problems.
The Deep Learning is one of most accurate machine learning technologies that develops model to match human like intelligence. In this section you will see what are the various applications of Deep Learning in the industry? We will also explore how the Deep Learning technologies are solving complex machine learning problem?
Deep learning technology is growing very fast as now we have faster CPU and GPU to train deep learning models with very large data sets. As compare to the previous days, now the deep neural network training time is reduced to days instead of weeks. Hardware manufacturers are developing faster hardware for training deep neural networks. With the advent of large data collection and storage technologies the scope of deep learning increased a lot.
Deep learning is being used in almost all the fields including manufacturing, healthcare, defense, retail, video production, music industry, cyber security and many more. Today we are going to see the top 15 applications of deep learning in the industry.
Now we will see the top applications of Deep learning in the industry.
The manufacturing of self-driving cars is one of top research projects these days. The researchers are going on for manufacturing viable self-driving cars by top technology giants. We have seen the self-driving cars in the movies; now with the help of Deep learning technologies it is going to be true in coming years. Soon car manufacturing companies like Tesla, Nissan, Cadillac are expected to come with the self-driving cars in the market.
Deep Learning is the heart of development of self-driving car, here millions of data sets are feed to the system to learn and develop a model to run the car. This research is huge work as it requires a lot of safety testing and taking approval from the authorities to bring it on the road. The Uber AI Labs at Pittsburg is working on the self-driving car that can even used for delivery of goods. Data scientist are testing several smart features that can be integrated with the self-driving car for better results.
The self-driving cars uses top technologies such as computer vision, gps navigation, deep neural networks and self learning. System can use the 2-D maps for navigation and other means for finding out the objects while moving on the road.
The CNN and LSTM based trained model can be used to add sound to the mute movies based on the video content. For training such model pre-recorded audio/video can be used and generate intelligent model. Such model can be used to add synthetic sound to the mute movie.
The deep learning is ideal solution for such type of work. Many companies are doing research on this for generating synthetic audio for the videos.
The News is very sensitive data in our age and its authenticity is very important. Based on the news from various sectors stock market and essentials thins in the day-to-day life is affected. So, once fake news can do a lots of destruction to the economy or society.
The News Aggregation and analysis is one of the area where Deep learning technologies are used. It helps data scientists to understand the intent of the news with the help of NLP and topic modeling. Various mathematical algorithms and deep learning techniques are used to find the authenticity of the News.
Facebook, Twitter, What's app and other 1000s of News portal around the world is generating text contents and it is important for the distributing agency to find out fake News and stop its distribution. Social networking giants, Google News, Yahoo News and other such services are using deep learning algorithms to filter out fake News to save the society.
The Deep Learning technologies helps data scientists to develop classifiers to detect fake or biased news in real-time. It is very import to filter out these Fake or biased news before it goes viral on the Internet. Deep learning models can be developed by training it with the large data sets to accurately identify fake news.
There are many languages around the world and it will be nice if there is a tool for translating it in different language. The language translation is one of most researched top of last decade and now deep learning is powerful enough to provide highly accurate results. Google and other technology giants have developed trained model to translate text to another lanugage.
The Natural Language Processing or NLP for short is the use of machine learning technologies to understand the written/spoken language to find out syntax, semantics, tonal nuances, expressions, meaning. Human learns language from its parents and society. But in case of machine it is the various NLP technologies which is used to train model to understand the language. Here also Deep learning plays very important role. The deep learning neural network models can be developed by training it with the large amount of data, once model is trained it can understand the natural language. It can even do language translation, summarization, entity extraction and summarizing the text data.
The application of Deep learning in natural language processing helped the data scientist to develop applications that can translate one language text into another language. The famous example is Google language translator which can translate text to many other languages. Google uses Deep learning for training language conversion models which performs very well.
Deep learning models can be used for answering questions, language modeling, classifying text, sentimental analysis, document summarization, topic modeling, knowledge graph generation and many such works.
The deep learning technologies are powerful enough to work the text data. It can learn from tons of data and then build intelligent neural network model that can even generate text automatically. These models are so powerful that it can generate text word-by-word or/and character-by-character.
The deep learning model are capable of learning the grammar and produce grammatically correct sentences.
The Virtual Assistants is very popular these days and its application is very high in varied field. Popular virtual assistants like Alexa, Siri and Google Assistant are using deep learning technologies to develop human like intelligence. Virtual Assistants can understand human voice command and respond to the user using natural human language. These virtual assistants are highly intelligent and translate human voice into text.
All these are possible though advancement in Deep learning technologies where companies are building deep learning neural networks by training them with huge data set. These days virtual assistants are so intelligent that it can help in text generation, document summarization or assisting use by listing voice command.
Various entertainment based companies like VEVO, Netflix, Film Making, Sports Highlights, etc. are using Deep learning to create highly entertaining audio visual contents. For example Wimbledon 2018 used IBM Watson to hundreds of hours of videos to find various patterns such as player emotions. After analyzing the player emotions and expressions it was saved with the system to generate the videos later on for highlights. So, with these technologies it can be used to generate highly entertaining content for users.
Other companies like Netflix and Amazon are using deep learning technologies very extensively to deliver personalized content to the users. This way they are increasing their view time on their content and increasing the revenue.
The deep learning technologies are used to recognize the pattern in vast data sets and then server personalized content to the users. All these are possible only with the help of latest development of machine learning and deep learning technologies.
The visual recognition of image including face, eyes, face age and many other features can help in writing applications that can organize the old pictures of person by date. If this work is to be done manually then it will be very difficult because most of the old picture don't have meta-data associated with it. In scenario deep learning based solution can help. To solve this many companies like Google have developed deep learning model which can organize pictures of the user.
Apart from this there are many ways deep learning can be used in the Visual Recognition and Image processing. Deep learning module can sort out images based on the location detected, faces, combination of people and other features available in the image.
These days Python programming is most used programming language for develop program to train deep learning neural network. The programming library such as TensorFlow, Keras, PyTorch etc.. are extensively used in the industry.
In the financial domain Deep Learning is being used very heavily for various type of data analysis and intelligently detection various patterns in the data. Money being the core business; its necessary for Financial institutions to track down each financial transactions and grab if there is fraudulent transaction. Here Deep learning plays major role of detecting fraud in the transaction and alert the authorities of even stop the transactions. Banking industries are using Autoencoders in Keras and Tensorflow libraries to develop intelligent models to catch fraudulent transactions.
Models are developed considering the customer transactions and credit scores, identifying anomalous behavior and outliers in the transactions. All are done in real-time so such transactions are caught at right time saving millions of dollars of Bank.
So, deep learning is one of the promising technologies for Banking and Finance industry. Here pre-trained deep neural networks can be used which gives human like performance at very high speed.
The healthcare industries are using deep learning in many different ways to provide better healthcare to the society. For drug discovery it is used to analyze the structure of medicinal compounds and its interaction with the human body/clinical trials. It helps in developing better medicines for society.
Medical imaging is also very big fields where Deep learning technologies can be used for accurately identify diseases by examining various medical records. For example well trained deep neural network model can detect lunch cancer more accurately than the average medical professional. It can be used to assist pathologist/radiologists in examining the reports fast and correctly. For example if lung cancer medical image is given to the neural network then it will analyze and detect the parts of suspicion. Further doctors can analyze the results generated by system to accurately diagnosing the disease.
Various medical research data can be processed and analyzed very fast with the deep learning technologies. With the help of GPU data processing can be done very fast and in days instead of weeks.
Almost all the commercial website online today uses some kind of personalization for the user. For example they understand user preference and display personalized content for that user. The e-commerce giants like Amazon, E-Bay, Alibaba and others are using deep learning technologies to provide seamless personalized experiences to user. They display the products and products for cross sale based on the user's previous purchase statistics and preferences. The deep learning technologies can be used to categorized customers in unlimited way and then display personalized content based on many complex features.
Deep learning technologies helps data scientists to find out complex hidden feature in the tons of data and then use this for better customer experience.
The Deep learning technologies can be used very well to find out Speech disorders, autism, and developmental disorders in children's at right time. This helps better medical care for such type of children. The early detection of such disorders in children will help in providing medication at early stage and this proves to have wonderful effect on the physical, mental, and emotional health of the patient.
We have huge amount of data with the earth research centers and if deep learning is applied than it can detect the earthquakes in advance by analyzing current data recorded by earth centers. The Deep learning neural networks can be used to accurately calculate viscoelastic computations and provide the future predictions about possible earthquakes. This innovation will help in saving life of large number of people.
The Deep neural networks are very good at processing the images and classify them according to various features. Deep neural networks can be trained to accurately diagnose various types of cancer including lung cancer, brain cancer, prostate caner and so on. So, there is great future of deep learning for the development of intelligent tools for helping doctors in diagnosing diseases fast and accurately.
In this section we have discussed top 15 applications of deep learning in world.
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