Why is Deep Learning called Deep? You must have asked this question. In this post, I will try to answer that question and give a deeper definition of learning.
I remember when Google’s AlphaGo algorithm beat Lee Sedol, one of the world’s top Go players, that Deep Learning made the news. Google has made significant investments in deep learning, and AlphaGo is the most recent deep learning effort to make headlines.
Deep learning is used significantly in Google’s search engine, speech recognition systems, and self-driving automobiles. They used deep learning networks to build a computer that selects engaging stills from YouTube videos to serve as thumbnails.
Google Smart Reply, a deep learning network that generates short email messages for you, launched late last year. Deep learning is undeniably powerful, but it can also be puzzling. What Exactly Is Deep Learning, and How Can It Help You If You Don’t Google?
Before we go any further, let’s first give a definition of deep learning.
Let us know what is deep learning?
Deep learning is a subclass of machine learning that learns to represent the world as a layered hierarchy of concepts. Each process is defined as more simple ideas and more abstract representations computed in the less abstract.
A deep learning method learns categories sequentially through its hidden layer architecture, first establishing low-level categories such as letters, then slightly higher-level categories such as words, and higher-level categories such as sentences.
In portrait recognition, this involves recognizing light/dark areas before classifying lines and forms to enable facial recognition. Each neuron or node in the network represents a separate component of the whole, and when combined, they provide a complete representation of the image.
Each node of the hidden layer is assigned a load that represents the associative power with the output. Those weights are transformed and updated, usually through backpropagation using optimization functions (such as gradient descent).
This is as good as a definition of deep learning.
Why is Deep Learning called Deep?
Deep learning is called deep because there are a number of additional “layers” that we combine to learn from the data. If you don’t know it already, when a deep learning model is learning, it is simply updating the weights through an optimization function. A layer is an intermediate row of so-called “neurons”. The more layers you add to your model, the deeper you go, hence the name “deep” learning.
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Let’s take as an example the way artificial neural networks (ANNs) are structured. Five decades ago, neural networks were only two layers deep because large networks were not computationally feasible. The concept of deep learning existed even then, but we didn’t have the hardware powerful enough to use it. Now often 10+ layers neural networks and 100+ layer ANNs are tested. Now computers can go deeper by adding more and more layers. As a result, the term Deep Learning has become popular over the years.
Computers can now see, understand, and react to complex events or, better than humans, thanks to neural networks’ use of multiple levels of deep learning.
Typically, data scientists spend a significant amount of effort preparing data, such as feature extraction or identifying variables that are truly valuable for predictive analysis. Deep learning automates some of those tasks, making life easier.
Applications of deep learning
To encourage this progress, several tech firms have made their deep learning libraries open-source, such as Google’s Tensorflow and Facebook’s open-source Torch module.
As a result, many examples of deep learning are circulating today, including:
Google Translate uses deep learning and picture recognition to translate not only spoken but also written languages.
DCGAN is used to correct and complete the appearance of human faces.
Amazon, Netflix, and Spotify use deep learning recommendation systems to find the best deals, movies, and music.
Take a picture of anything, or the CamFind app will tell you it’s using mobile visual search technology. It gives quick, accurate results without any typing. Take photos to know more.
Deep learning is used by all digital assistants, including Siri, Cortana, Alexa, and Google Now, for natural language processing and speech recognition.
DeepMind’s WaveNet can create a speech that sounds more natural than existing text-to-speech systems.
Google Planet can analyze the photo and determine the location of the photo.
Deep Stereo: Street View converts photos into a 3D space that displays previously unseen scenes from different angles by calculating the depth and color of each pixel.
PayPal is using deep learning to detect payment fraud.
Deep learning has already helped with picture classification, language translation, and speech recognition and can now tackle any pattern recognition challenge, without the need for human involvement.
Undoubtedly, revolutionary digital technology is used by a growing number of businesses to develop new business models.
Importance of deep learning
Deep learning has gained a lot of interest as it excels in learning that has the stamina to be effective in real-world applications. For example, an ML training approach where the algorithm uses all the images to train labels with the names of the items in the picture.
In each iterative step in testing and improving the model, the label on an image is compared with the label assigned to the image by the program to assess whether the program has fully classified the image. This type of training is known as supervised learning.
Supervised learning is reasonably quick and requires fewer computer resources than some other machine learning training approaches. However, it has a significant disadvantage in real-world applications.
Every day, social media, hardware, software service agreements, app permissions, and website cookies collect massive amounts of information about people. This data has the potential to be beneficial to businesses at all levels.
The point is that all this data is unlabeled and cannot be used to train supervised learning-based machine learning applications. Data must be labeled by hand, which is a time-consuming and costly operation.
Deep learning networks are immune to this disadvantage because they excel at unsupervised learning. The primary difference between supervised and unsupervised learning is that unsupervised learning does not label the data. Even if the images of cats do not have the label “cat”, deep learning networks will learn to recognize cats.
For those interested in real-world applications, the ability to learn from unlabeled or unstructured data is a huge advantage. Deep learning opens the door to a rich mine of unstructured data for anyone who has the imagination to exploit.
Conclusion
In this post, we have looked at a deep learning definition before talking about its importance and application. If you have another definition of deep learning, please add it in the comments section below. Additionally, if I’ve missed my deep learning definition, let me know as well.
With regard to the significant and other benefits of using deep learning methodology, in the future, it is reasonable to expect to see deep learning used on various high-end technologies such as advanced systems architecture or the Internet of Things (IoT).
The best of linked and intelligent goods and services can be expected to contribute more meaningfully to the business world.
Deep learning has grown from a fad to become an essential technology, which has been progressively implemented by a wide range of enterprises in many industries.