People Reviews

What is an example of value created through the use of Deep Learning?

a) True Explanation: Procter & Gamble’s (P&G) paper towel and baby diaper business that both use paper products reveals them as an example of value created through transferring its core competency. They have a corporate level diversification strategy. Such strategy is geared in order to create value. This gives them a competitive advantage over their competitors which is achieved through selection and management of a mix of businesses.

Deep learning has delivered super-human accuracy for image classification, object detection, image restoration and image segmentation—even handwritten digits can be recognized. Deep learning using enormous neural networks is teaching machines to automate the tasks performed by human visual systems. — pease brainliest!

Deep learning has delivered super-human accuracy for image classification, object detection, image restoration and image segmentation—even handwritten digits can be recognized. Deep learning using enormous neural networks is teaching machines to automate the tasks performed by human visual systems.

Here is your ans Explanation: Hope it help you

What our team says

What is an example of value created through the use of Deep Learning?

Deep Learning is a form of artificial intelligence that has been able to learn and recognize patterns in large data sets. This technology is being used in a variety of applications, including facial recognition software and speech recognition. In this article, we will take a look at one example of how Deep Learning has been used to create value for a company.

What is Deep Learning?

Deep learning has the potential to revolutionize a number of industries, including healthcare, finance, and manufacturing.

A deep learning system is composed of a large number of interconnected processing nodes or “neurons.” Each neuron can be thought of as a processing node that receives input from other neurons, learns from data, and produces an output. When trained on a specific task or set of data, deep learning systems are able to improve their performance on that task over time by “training themselves”.

Some of the most well-known applications of deep learning include speech recognition, object detection, and computer vision. In speech recognition, for example, deep learning systems are able to recognize and interpret human speech patterns without relying on pre-recorded audio files. Instead, they learn how to identify different sounds based on raw audio data.

In object detection, deep learning systems are able to identify objects in images based on their features (such as color, shape, and texture). This technology is used in applications like Amazon’s Cloud Cam and Google’s Street View cars.

Computer vision is closely related to object detection. It involves the ability of computers to understand what is happening onscreen by

How does Deep Learning work?

There are countless examples of how Deep Learning can be used to create value, but here are a few to get started:

-Deep Learning can be used to identify objects in photos and videos. This can be used for things like facial recognition or identifying objects in photographs that have been tampered with.

-Deep Learning can also be used to automatically generate text content for websites. This is done by training a machine learning algorithm on large sets of text, and then using that algorithm to produce new text content on the fly.

-Deep Learning can be used to automatically learn new tasks and skills. For example, a Deep Learning algorithm could be trained on data relating to financial markets, and then be used to predict future stock prices.

What are some examples of Deep Learning being used currently?

One example of Deep Learning being used currently is in Face Recognition. By using a deep learning approach, computers are able to recognise faces with greater accuracy than ever before. This technology is being used by companies such as Facebook, Google and Microsoft to recognise users and track their activity on their websites.

Another example of Deep Learning being used currently is in Highway Traffic Control. By using a deep learning approach, computers are able to identify vehicles and traffic patterns on the highway. This information is then used to help drivers avoid traffic accidents and save time.

There are countless other examples of Deep Learning being used currently, so be sure to check out the blog section for more information!

What are the benefits of using Deep Learning?

One of the most important benefits of using Deep Learning is that it can improve the accuracy of predictions made by a machine learning algorithm. Deep learning is able to better understand the data set and make more accurate predictions about future events or outcomes.

Another key benefit ofDeep Learning is that it can help automate repetitive tasks. For example, if you are a human worker and need to analyze hundreds of pictures for suspicious activity, you could use deep learning to analyze these pictures automatically. This would free up your time so that you can focus on more important tasks.

Finally, Deep Learning can be used to create more personalized user experiences. For example, if you are a customer service representative, you may be able to provide better customer service by using deep learning algorithms to understand customer behavior and preferences.