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Deep Learning

What Is Deep Learning?

Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. It’s a subset of machine learning that has networks capable of learning unsupervised from unstructured or unlabeled data. It’s also known as deep neural learning or deep neural network.

Deep learning AI can learn without human supervision, drawing from data that is both unstructured and unlabeled.

What's the difference between Machine Learning and Deep Learning?

Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically.

Another key difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network.

A key advantage of deep learning networks is that they often continue to improve as the size of your data increases.

How does Deep Learning work?

Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms. This data, known simply as big data, is drawn from social media, internet search engines, eCommerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through FinTech applications like cloud computing.

However, the data is typically unstructured and so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential of unraveling this wealth of information and are increasingly adapting to AI systems for automated support.

 

Types of deep learning algorithms

A growing number of deep learning algorithms make these new goals reachable. We’ll cover two here just to illustrate some of the ways that data scientists and engineers are going about applying deep learning in the field.

Convolutional Neural Networks

Convolutional neural networks are specially built algorithms designed to work with images. The ‘convolution’ in the title is the process that applies a weight-based filter across every element of an image, helping the computer to understand and react to elements within the picture itself. 

This can be helpful when you need to scan a high volume of images for a specific item or feature; for example, images of the ocean floor for signs of a shipwreck, or a photo of a crowd for a single person’s face. 

This science of computer image/video analysis and comprehension is called ‘computer vision’, and represents a high-growth area in the industry over the past 10 years.

Recurrent Neural Networks

Recurrent neural networks, meanwhile, introduce a key element into machine learning that is absent in simpler algorithms: memory. The computer is able to keep past data points and decisions ‘in mind’, and consider them when reviewing current data – introducing the power of context.

This has made recurrent neural networks a major focus for natural language processing work. Like with a human, the computer will do a better job understanding a section of text if it has access to the tone and content that came before it. Likewise, driving directions can be more accurate if the computer ‘remembers’ that everyone following a recommended route on a Saturday night takes twice as long to get where they are going.

A deep learning use case

Using the fraud detection system mentioned above with machine learning, one can create a deep learning example. If the machine learning system created a model with parameters built around the number of dollars a user sends or receives, the deep-learning method can start building on the results offered by machine learning.

Each layer of its neural network builds on its previous layer with added data like a retailer, sender, user, social media event, credit score, IP address, and a host of other features that may take years to connect together if processed by a human being. Deep learning algorithms are trained to not just create patterns from all transactions, but also know when a pattern is signaling the need for a fraudulent investigation. The final layer relays a signal to an analyst who may freeze the user’s account until all pending investigations are finalized.

Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition, open-source platforms with consumer recommendation apps, and medical research tools that explore the possibility of reusing drugs for new ailments are a few of the examples of deep learning incorporation.

Other deep learning applications

1. Virtual Assistants

The core functionality that requires translating the speech and language of the human’s speech, is deep learning. The common examples of virtual assistants are Cortana, Siri, and Alexa.

2. Vision for Driverless, Autonomous Cars

In order to navigate an autonomous car, say, a Tesla, one needs a human-like experience and expertise. 

To understand the scenarios of roads, the working of signals, pedestrians, significances of different signs, speed limits, and many more situations like these, a large amount of real data is required.

With the large data, the efficiency of the algorithms will be improved which will subsequently increase the decision-making flows.

3. Service and chatbots

The continuous interaction of chatbots with human beings for providing customer services requires strong responses. 

To respond in a helpful manner to all the tricky questions and apt responses, deep learning is required for training algorithms.

4. Translations

Translating the speech automatically in multiple languages requires deep learning supervision. This is a helpful mechanism for tourists, travelers, and government officials. 

5. Facial Recognition

Facial recognition has many features from being used in the security to the tagging mechanism/feature used on Facebook. 

Along with the importance, it has its fair share of issues as well. For example, to recognize the same person with weight gain, weight loss, beard, without a beard, new hairstyles, etc. 

6. Shopping and Entertainment

All the shopping applications like Amazon and Myntra and entertainment applications like Amazon Prime and Netflix store your data and buying habits to show the suggestions for future buying and watching. 

It always comes as a title “You may like to watch/buy”. The more data is inputted in the Deep learning algorithm, the more efficient it becomes in decision making. 

7. Pharmaceuticals

Customizing medicines based on the particular genome and diseases. Deep learning has widened the scope of such applications and has gained the attention of the largest pharmaceutical companies. 

Besides that, other deep learning applications are fraud detection, virtual recognition, healthcare, entertainment and many more.

About Engati

Engati powers 45,000+ chatbot & live chat solutions in 50+ languages across the world.

We aim to empower you to create the best customer experiences you could imagine. 

So, are you ready to create unbelievably smooth experiences?

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Deep Learning

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What Is Deep Learning?

Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. It’s a subset of machine learning that has networks capable of learning unsupervised from unstructured or unlabeled data. It’s also known as deep neural learning or deep neural network.

Deep learning AI can learn without human supervision, drawing from data that is both unstructured and unlabeled.

What's the difference between Machine Learning and Deep Learning?

Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically.

Another key difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network.

A key advantage of deep learning networks is that they often continue to improve as the size of your data increases.

How does Deep Learning work?

Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms. This data, known simply as big data, is drawn from social media, internet search engines, eCommerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through FinTech applications like cloud computing.

However, the data is typically unstructured and so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential of unraveling this wealth of information and are increasingly adapting to AI systems for automated support.

 

Types of deep learning algorithms

A growing number of deep learning algorithms make these new goals reachable. We’ll cover two here just to illustrate some of the ways that data scientists and engineers are going about applying deep learning in the field.

Convolutional Neural Networks

Convolutional neural networks are specially built algorithms designed to work with images. The ‘convolution’ in the title is the process that applies a weight-based filter across every element of an image, helping the computer to understand and react to elements within the picture itself. 

This can be helpful when you need to scan a high volume of images for a specific item or feature; for example, images of the ocean floor for signs of a shipwreck, or a photo of a crowd for a single person’s face. 

This science of computer image/video analysis and comprehension is called ‘computer vision’, and represents a high-growth area in the industry over the past 10 years.

Recurrent Neural Networks

Recurrent neural networks, meanwhile, introduce a key element into machine learning that is absent in simpler algorithms: memory. The computer is able to keep past data points and decisions ‘in mind’, and consider them when reviewing current data – introducing the power of context.

This has made recurrent neural networks a major focus for natural language processing work. Like with a human, the computer will do a better job understanding a section of text if it has access to the tone and content that came before it. Likewise, driving directions can be more accurate if the computer ‘remembers’ that everyone following a recommended route on a Saturday night takes twice as long to get where they are going.

A deep learning use case

Using the fraud detection system mentioned above with machine learning, one can create a deep learning example. If the machine learning system created a model with parameters built around the number of dollars a user sends or receives, the deep-learning method can start building on the results offered by machine learning.

Each layer of its neural network builds on its previous layer with added data like a retailer, sender, user, social media event, credit score, IP address, and a host of other features that may take years to connect together if processed by a human being. Deep learning algorithms are trained to not just create patterns from all transactions, but also know when a pattern is signaling the need for a fraudulent investigation. The final layer relays a signal to an analyst who may freeze the user’s account until all pending investigations are finalized.

Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition, open-source platforms with consumer recommendation apps, and medical research tools that explore the possibility of reusing drugs for new ailments are a few of the examples of deep learning incorporation.

Other deep learning applications

1. Virtual Assistants

The core functionality that requires translating the speech and language of the human’s speech, is deep learning. The common examples of virtual assistants are Cortana, Siri, and Alexa.

2. Vision for Driverless, Autonomous Cars

In order to navigate an autonomous car, say, a Tesla, one needs a human-like experience and expertise. 

To understand the scenarios of roads, the working of signals, pedestrians, significances of different signs, speed limits, and many more situations like these, a large amount of real data is required.

With the large data, the efficiency of the algorithms will be improved which will subsequently increase the decision-making flows.

3. Service and chatbots

The continuous interaction of chatbots with human beings for providing customer services requires strong responses. 

To respond in a helpful manner to all the tricky questions and apt responses, deep learning is required for training algorithms.

4. Translations

Translating the speech automatically in multiple languages requires deep learning supervision. This is a helpful mechanism for tourists, travelers, and government officials. 

5. Facial Recognition

Facial recognition has many features from being used in the security to the tagging mechanism/feature used on Facebook. 

Along with the importance, it has its fair share of issues as well. For example, to recognize the same person with weight gain, weight loss, beard, without a beard, new hairstyles, etc. 

6. Shopping and Entertainment

All the shopping applications like Amazon and Myntra and entertainment applications like Amazon Prime and Netflix store your data and buying habits to show the suggestions for future buying and watching. 

It always comes as a title “You may like to watch/buy”. The more data is inputted in the Deep learning algorithm, the more efficient it becomes in decision making. 

7. Pharmaceuticals

Customizing medicines based on the particular genome and diseases. Deep learning has widened the scope of such applications and has gained the attention of the largest pharmaceutical companies. 

Besides that, other deep learning applications are fraud detection, virtual recognition, healthcare, entertainment and many more.

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