Request for free trial
ddep-Icon1.png

What is
Deep Learning

Deep learning, a new area of machine learning, is most widely based on large-scale convolutional neural networks composed of complex model architectures that processes data such as images, video, sound and text.

These set of algorithms contains multiple layers of non-linear transformations that learn and improve in accuracy and latency over time to make sense of massive volumes of data - all by itself without the need for direct human intervention.

Deep learning algorithms: How they ‘learn’

Convolutional neural networks

These deep learning networks give computers the ability to interpret the context of real-world situations and to understand the human world.

Inspired by the neural connections in the human brain, they employ a layered approach to developing understanding of data, and are trained to learn and change over time.

Backpropagation

In this process, the software steps back through each of the previous layers, each time tweaking the mathematical expression for that layer, just enough so that it would get the answer right the next time.

At first the network will make mistakes all the time, but gradually its performance improves as the layers become better at discerning what they see.

This iterative process of passing a data sample through the network and backpropagation to correct itself is the essence of deep learning, and it’s what imbues the networks with intelligence and the nuanced understanding of what confronts them.

Armed with all the available amount of data these days, neural networks can grow ever more intelligent.

Algorithmic layers

These biologically-inspired software use one algorithm to process insight about an input, then pass it on to the next layer to process using a different algorithm to gain some higher-level understanding, and so on.

The different layers of mathematical processing exist to make ever more sense of the information they’re fed, from human speech to a digital image.

In other words, the algorithmic layers of deep neural networks act as cascaded mathematical equations that can spot distinctive features and patterns from abstract data.

Training and learning

To learn and train themselves, neural networks need large quantities of information to consider, pass through their layers, and attempt to classify.

During training, the machine’s answer is compared to the human-created description of what should have been observed. If it’s right, props to the networks. If it’s wrong, it uses a technique called backpropagation to adjust itself.

Deep learning Applications and Uses

Like it or not, deep learning technology is rapidly transforming the way we work with images, text, videos and voice.
Its applications are entering more mainstream adoption in natural language processing, image recognition, and rapid video analysis, and are becoming commonplace elements in consumer-facing services most of us use on a daily basis.

Image Recognition
Automatic Speech Recognition
Natural Language Processing
Customer Relationship Management

Image Recognition

This is most common and popular use case of deep neural networks, where the algorithms are trained on large image datasets to classify them to reach sufficient accuracy (achieving almost human image recognition and later surpassing humans).

Medical Imaging

For instance, in mammography, deep learning meets medical imaging in the interpretation of radiologic images to successfully detect or characterize abnormalities on digital images.

Radiologists supplied with this information often perform better at mammographic detection or characterization tasks in observer studies than do unaided radiologists.

Deep learning therefore could decrease errors in mammographic interpretation that continue to plague human observers.

Security

Handwriting analysis for banks are definitely in the works, and not forgetting facial recognition for security purposes at both Apple and MasterCard, where users are able to unlock future iPhones and verify credit card payments by taking a selfie.

E-Commerce

By feeding the neural networks with large amounts of data, the machines are able to identify, tag and describe the objects found in images, regardless of the database size.

This goes towards saving man hours during product tagging and attribution extraction processes in the e-comm...vity for these players.

Automobile

We are already seeing self-driving cars, where deep learning software integrates with automated driving systems to detect and interpret real-life situations on the road.

Automatic speech recognition

Speech recognition, the process of enabling a computer to identify and transcribe spoken language into readable text in real time, is advancing fast.

For example, applications like Siri or Cortana needs no introduction - they transcribe human speech (such as short utterances of commands, questions, or dictations) into text.

Deep learning technologies are advancing fast in in voice recognition for calling systems, where voice recognition systems could become so advanced that telemarketing campaigns will be run by software alone.

Skype now uses neural networks to translate from one language to another on the fly.

Natural language processing

Referring to the use of computers to analyze natural language for any number of purposes, natural language processing holds the possibility for deep learning software to understand the contextual meaning of text that people type or say.

This application can also be used for processing and analysing chat room conversations or text from human speeches.

Companies like Facebook and Google are very interested because this is important for them to provide better user interfaces, advertisements, and posts for users’ news feed by using question and intent analysis to analyze text, such as working on syntax, semantics, knowledge extraction, summarization and question answering.

Customer relationship management

Recent success has been achieved with the application of deep learning in direct marketing settings, powering CRM automation. Current neural networks are able to help sales and marketing people monitor relationships with clients or prospects.

For example, they could flag an important customer and recommend a call or other outreach if there had been no communication with that lead in a set amount of time, by evaluating incoming data and adjusting algorithms to ensure the best outcome.

Neural networks are finally giving computers the ability to understand the human world, and make smart inferences about it. With the available hardware and resources, neural networks are developing very rapidly.

Image Recognition

This is most common and popular use case of deep neural networks, where the algorithms are trained on large image datasets to classify them to reach sufficient accuracy (achieving almost human image recognition and later surpassing humans).

Medical Imaging

For instance, in mammography, deep learning meets medical imaging in the interpretation of radiologic images to successfully detect or characterize abnormalities on digital images.

Radiologists supplied with this information often perform better at mammographic detection or characterization tasks in observer studies than do unaided radiologists.

Deep learning therefore could decrease errors in mammographic interpretation that continue to plague human observers.

Security

Handwriting analysis for banks are definitely in the works, and not forgetting facial recognition for security purposes at both Apple and MasterCard, where users are able to unlock future iPhones and verify credit card payments by taking a selfie.

E-Commerce

By feeding the neural networks with large amounts of data, the machines are able to identify, tag and describe the objects found in images, regardless of the database size.

This goes towards saving man hours during product tagging and attribution extraction processes in the e-comm...vity for these players.

Automobile

We are already seeing self-driving cars, where deep learning software integrates with automated driving systems to detect and interpret real-life situations on the road.

Automatic speech recognition

Speech recognition, the process of enabling a computer to identify and transcribe spoken language into readable text in real time, is advancing fast.

For example, applications like Siri or Cortana needs no introduction - they transcribe human speech (such as short utterances of commands, questions, or dictations) into text.

Deep learning technologies are advancing fast in in voice recognition for calling systems, where voice recognition systems could become so advanced that telemarketing campaigns will be run by software alone.

Skype now uses neural networks to translate from one language to another on the fly.

Natural language processing

Referring to the use of computers to analyze natural language for any number of purposes, natural language processing holds the possibility for deep learning software to understand the contextual meaning of text that people type or say.

This application can also be used for processing and analysing chat room conversations or text from human speeches.

Companies like Facebook and Google are very interested because this is important for them to provide better user interfaces, advertisements, and posts for users’ news feed by using question and intent analysis to analyze text, such as working on syntax, semantics, knowledge extraction, summarization and question answering.

Customer relationship management

Recent success has been achieved with the application of deep learning in direct marketing settings, powering CRM automation. Current neural networks are able to help sales and marketing people monitor relationships with clients or prospects.

For example, they could flag an important customer and recommend a call or other outreach if there had been no communication with that lead in a set amount of time, by evaluating incoming data and adjusting algorithms to ensure the best outcome.

Neural networks are finally giving computers the ability to understand the human world, and make smart inferences about it. With the available hardware and resources, neural networks are developing very rapidly.

Why is deep learning the buzzword nowadays

Everyone wants in on the Deep learning Game

Titans of the web such as Google, Baidu, Yahoo, and Facebook have been pushing the deep learning envelope with their recent developments in algorithmic techniques.

Deep learning is hardly a new field, but it has become increasingly important in recent years as companies and users are wrestling with such huge volumes of data that cannot be analysed and organized with any kind of speed by human beings.

For example, at Google, Stanford computer science professor Andrew Ng founded the Google Brain project, which created a neural network trained with deep learning algorithms, which famously proved capable of recognizing high level concepts, such as cats, after watching just YouTube videos - and without ever having been told what a “cat” is.

And last year, Facebook named computer scientist Yann LeCun as its new director of AI Research, using deep learning expertise to help create solutions that will better identify faces and objects in the 350 million photos and videos uploaded to Facebook each day.

The expectation is that most companies will make use of image, video, and text recognition software on their datasets at some point, just like how everyone believes that ultimately they will use a mix of operational, transactional, and other kinds of unstructured data and complex analytics software to help them find and retail customers or run their businesses better.

icon2.png

Deep Learning And The Future

Despite it’s recent advances in areas such as object perception and voice recognition, deep learning is still a young field with so much potential to create an impact on users’ lives.

The technology is already being used to mold our lives and behavior, and the fact that we have greater access to the web through our various connected devices - smartphones and tablets, laptops and desktops, and wearable technology - gives companies far greater ability to improve lives.

Neural networks have come a long way in the last several years on the algorithmic and hardware side, but the real key to pushing their performance and development lies in taking full advantage of their application to real-life situations.

ViSenze - Pioneer in Deep Learning & Computer Vision

At ViSenze, we develop techniques to train deep neural networks and apply them to real-world problems, starting with providing visual search and image recognition solutions for the fashion e-commerce vertical.

To achieve that, we have developed deep neural networks using different layers of mathematical processing to gain higher levels of understanding of images.

They are able to locate objects in both videos and static images, carry out foreground extraction and perform other smart procedures on the visual content to make sense of it.

For more information on our technology and how it makes sense of the booming visual web, click here.

Find our more about ViSenze's visual technology

Read more

New Call-to-action