Computing is rapidly revolutionizing and at the center of this revolution lies artificial intelligence and machine Learning. Both these technologies, and the ones branching out from these, are hinged on their feature to recognize patterns in data. Think about the streaming suggestions by Netflix or the shopping cues by Amazon. Most machines using AI principles are smart, but do not have the prowess to learn on their own. Hence, making human intervention necessary. Deep Learning, on the contrary, is fully capable on its own. But what is deep learning and why is it such a hyped technology?

Let’s decode everything about it!

What Is Deep Learning?

So, what is deep learning? Deep learning (DL) is a division of machine learning and falls within the category of artificial intelligence. The DL technology works by teaching a computer model to follow the principle of learning by example. It is similar to how a child learns from the behavior and actions of teachers and parents.

It supports and works with all kinds of data such as images, text, audio, etc. Hence, the computer is tuned to read and process data much like a human brain. This is done by using neural networks (NN).

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What are Neural Networks?

Neural networks (NNs) refer to computational models that work in the same way as a human brain functions. These comprise of interconnected neurons or nodes that know how to process and learn from data. Hence, paving the path for tasks like decision making and pattern recognition.

These have an imperative task to play with their sea of abilities. These include identifying patterns, adjusting to the surrounding changes, and solving intricate puzzles. This capacity to learn from data is revolutionary and has led to the evolution of various technologies.

What Is Machine Learning vs Deep Learning?

Now that you know ‘what is deep learning’, you know that it is a subset of machine learning. However, do you know what is machine learning vs deep learning? What exactly makes these two different from one another? Let’s find out!

Examples of Deep Learning

DL has branched out significantly and today, it is being used in different industries for different tasks. Some of the most commonly found examples of DL are listed here.

1. Self-Driving Cars

Autonomous cars are already swifting down the road and that has been possible due to deep learning with TensorFlow. A driverless car is no longer a work of fiction because of the millions of scenarios it has been exposed to.

2. Facial Recognition

The most common example is the iPhone’s facial recognition system that uses DL to identify your facial data points and unlocks the phone. It is not negatively affected by factors like gaining, loss, growth, or chopping of hair, weight, or beard.

3. Personalization

Ever wondered how Netflix knows you like sci-fi or how Amazon knows you might be interested in red t-shirts? It is all thanks to the DL capabilities that they are investing in more and more. In fact, various organizations are expected to work around DL in the coming years.

4. Virtual Assistants

Siri, Google Assistant, Alexa, and Amazon Echo are all great examples of virtual assistants. These learn to recognize voices and accents to offer a secondary human experience.

5. Medical Science

Fitness bands, virtual assistants, and gears are only a few examples of computers recording data to learn about our mental and physiological conditions. This leads to detection and personalized healthcare.

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Deep Learning Career- Skills Needed

One of the most promising deep learning career paths is that of a DL engineer. To become one, you must gain the skill set that is essential to land a job in this position. Here are certain key deep learning engineer skills that you must aim at achieving.

1. Data Modeling & Evaluation

Data modeling and evaluation skills are a must to become deep learning engineer. Data modeling aids in comprehending complex data structures to find patterns that are often overlooked by humans.

2. Programming Languages

Having a hold of programming languages that are compatible with different AI and ML tools is important. Some of the most widely used ones are Python and C++. DL with Python is a brilliant combination to learn.

3. Natural Language Processing (NLP)

Natural language processing refers to a system that facilitates computers in understanding human language, which is the entire idea behind AI. It’s important to gain knowledge of the different NLP libraries.

4. Mathematical Skills

It is important to possess good mathematical skills as there are plenty of use cases in DL. Various events arise when mathematical formulas are needed to develop deep learning algorithms. Fields like statistics, calculus, probability, and linear algebra are used extensively.

5. Knowledge of Neural Networks

Neural networks are the driving force behind DL. An in-depth fundamental knowledge of this aspect is necessary to help further explore it.

Wrap-Up

This can be said to be the best time to begin your journey in this technology. Our blog has covered what is deep learning, along with a few aspects around it. However, this is a vast field and the more you learn about it, the more you will realize its true potential.

If you are keen on learning this technology for a bright future, you should begin by learning essential skills, techniques, and tools. Tap into this expanding field and grow with it.

Frequently Asked Questions for What Is Deep Learning

Question 1. What is TensorFlow?

So, what is TensorFlow? TensorFlow refers to an end-to-end DL framework. This open source framework was developed by Google. It is famous for offering scalable deployment and production options, supporting various platforms, training and documentation support, and having multiple abstraction levels.

Question 2. What is CNN in deep learning?

CNN in deep learning stands for Convolutional Neural Network. It is a kind of artificial neural network employed for object/image classification and recognition.

Question 3. What is deep learning in simple words?

Deep learning is an artificial intelligence method for training computers on processing data in the same manner a human brain works.

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