“You May Also Like!” Have you often come across this line and wondered what exactly triggers it? Whether it is amazon giving you options featuring products similar to recent purchases or Netflix showcasing options that resemble the ones that you have watched lately, Deep Learning AI is the short and sweet answer to it!
Deep Learning is the brain behind the technology that remits the most frequently used phrase – “You May Also Like!”
We hope you may like also like to dive deep into Deep Learning techniques and find out how Deep Learning works?
A window to Deep Learning AI!
While Amazon and Netflix’s “Next-Watch” options are just small illustrations of Deep Learning, it extends far beyond these implementations. Deep Learning AI algorithms drive the self-driving cars of the day! Virtual assistants, facial recognition, & chatbots are all the currently trending examples of the same.
In short, they are creeping into every field and beyond in multiple facets of human lives. We will surely take this discussion forward further ahead.
To the core, Deep learning is an AI function, more often referred to as its subset.
The Deep Learning process trains machines to work in ways that come naturally to humans. Learn by example, i.e., carry forward the learning to make life better.
A deep learning AI helps machines to differentiate tasks directly from images, text, or sound. In short, Deep Learning models process data for implementation in identifying objects, recognizing speech, language translations, and above all, making decisions.
How deep learning delivers such profound results?
Deep learning methods typically use neural network structures also called artificial neural networks(ANNs). Therefore, deep learning also goes with the term deep neural networks.
During the formation of deep learning AI models, to increase the efficiency of the learning process these ANNs continuously receive learning algorithms. They keep growing their knowledge database, thereby increasing the performance. These structures tend to put the pieces together directly from the data with no major external extractions required.
Thus, the Deep learning process absorbs understanding without human guidance. Deep learning models can form perception even from the data that is disorganized or unlabeled. It achieves this with precision, sometimes exceeding human-level performance.
Well, the graph is directly proportional. As in the larger data volumes deliver more accuracy. The learning process is called deep for the right reasons because, with the passing time, a neural network grows to include a larger number of levels. Here deeper the network penetrates, the precision increases.
Deep Learning- self-improving!
Deep learning systems need compelling hardware. It is because they have a massive amount of data to be processed. The process entails several complex mathematical predictions. Even with such complicated calculations and complex hardware, the process of training can take weeks.
While processing data, the artificial neural networks classify data with the results obtained from a series of binary true or false questions.
For example, the list of movies to be showcased in your Netflix account. Initially, when you create a Netflix account and login into the system, it asks you to enter your preferences.
Say, you like watching comedy and romance, maybe in Hollywood. Then partaking your preferences, the initial Netflix page will display a list of ‘you may like stuff’. In short, over time, your account grows to signify your preferences when you keep using your account and watch a multitude of movies.
Deep learning processes majorly fit into two key aspects
Training is a process of designating large amounts of data and recognizing their similar properties. The deep learning systems feed on those characteristics. They compare and memorize these aspects. The next time they come across any similar data, then they estimate suitable conclusions.
Inferring, however, implements the knowledge it gained previously to make conclusions and identify unexposed data.
What is the difference between machine learning and deep learning AI?
Deep learning initially came into the picture as an advent to machine learning. On the other hand, it is now tracking more attention than the latter.
Machine learning is a process wherein large datasets get loaded into machine memory.
These machines then process knowledge from the laden dataset. Machine learning further involves specialists, as in human inference, who rectify any errors that occur while the machine processes.
In other words, the machine learning approach showcases a machine interpreting a notable amount of tasks. However, it mandates human intervention herein. As against this, deep learning becomes better every day. It can bring in solutions without much or no human mediation.
To put it straight, the differences between deep learning AI and machine learning are as follows:
Deep learning AI targets end-to-end operations. However, machine learning delivers results by dividing the tasks into smaller chunks and then combining the pieces.
Deep learning models are proficient enough. They need no human intervention, even for creating new features. But machine learning approach requisites human mediation in identifying and curating additional features.
Examples of deep learning in real-world scenarios
Let us walk through some live examples of
The deep learning process is the prime technology imbibed in self-driving cars. It empowers them to identify the signals and traffic rules.
These cars can also distinguish a pedestrian from a stationary object. Say, a tree or a traffic signal. This is nothing but object recognition and identification technology supported by deep learning AI.
Object recognition and detection:
Recognizing not only voices but objects is the excellent power of deep learning. Recognizing and identifying things in your surroundings via webcams is another brilliant illustration of technology in our day-to-day life.
Voice control in consumer devices:
Automatic speech recognition and responses in consumer devices like phones, televisions, and hands-free speakers are the best illustrations of deep learning.
Deep Learning Challenges
Like, there are two sides to every coin. Similarly, the technology of deep learning AI has a large bandwidth of goodness attached. But there seem to be countable flaws lined up with the technology.
Here’s an illustration of it!
Late last year, a YouTuber, who popularly created videos on chess, found himself in a fix to see his channel blocked. Although his channel went active within the next 24 hours, it was unexpected and shocking at the same time! Charges applied to the videos were harmful and dangerous content.
Now, it’s one of the deep learning flaws, as researchers suspected that the usage of words like ‘black’, ‘white’, ‘attack’, and ‘threat’ was confused with racist language and flagged for hate speech.
Another major disadvantage of deep learning technology is that it is very resource-demanding! Yes, deep learning primarily matures with continuous input of data. So, constant input of data is needed to achieve higher accuracy.
Deep Learning Career Opportunities
A technology so vibrant is paving the way to tremendous opportunities. There are numerous opportunities lined up! And, with very few heads filling in. Most importantly, AI is turning out to be one of the top careers of 2021.
Deep learning is evolving every hour. The digital transformations and technology-driven companies are making the stakes high for deep learning professionals
That said, you can expect recognized deep learning jobs in a multitude of companies.
Well, this is the best time to start learning this promising technology to make yourself future-ready. A profession so charming is very prompting!
Explore the course in deep learning with ZEN and understand the future aspects and possibilities.
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