Deep Learning Definition | How Does It Work?

In this article, you’ll learn what deep learning is, why is it called deep learning, how does it work and real-world examples of deep learning.

What is Deep Learning?

Deep learning is also known as deep neural learning or deep neural networking and it’s a branch of machine learning. Basically it’s a neural network with a layered hierarchy of concepts. These neural networks strive to replicate the behavior of the human brain but far from its capabilities.

It allows neural networks to learn from enormous amounts of data to make approximate predictions and extra hidden layers of a neural network can help to get accurate results.

Why Is It Called Deep Learning?

Deep learning represents the number of extra layers added to understand the data more accurately. When the deep learning model is in the learning phase, it updates the weights via an optimization function. A layer is simply an intermediate row made of neurons. The more you add the layers to the model while learning, the deeper it goes. That’s why it is popularly known as deep learning.

📌 Relevant: Explore other technology glossary terms

How Does Deep Learning Work?

Deep learning neural networks simulate the functioning of the human brain via a union of data inputs, weights, and biases. These components involve working together to detect, categorize and characterize the data objects accurately and productively. 

Deep neural networks (DNN) are stacked neural networks comprising multiple layers of interconnected nodes. Each layer is constructed upon the preceding layer to refine and advance the predictability or classification, and this computational advancement through the network is called forward propagation.

The two layers given below are the visible layers of the deep neural network:

  • Input layer – A deep learning model operates insertion of data.
  • Output layer – The output layer works in the ultimate prediction or categorization. 

Contrary to the forward propagation, the backward propagation engages methodologies such as gradient descent that computes mistakes in predictions and alters the weights and biases of the function by backtracking through the layers for training the model. 

The computational progressions – forward propagation and backward propagation function together to allow a neural network to make predictions and repair the errors correspondingly. Gradually, the accuracy of the algorithm grows in time. 

The statements above describe the simple type of deep neural networks in the simplest terms. Nevertheless, deep learning algorithms are highly complicated as there are various neural network types to answer clear-cut problems or datasets. 

Considering some example:

  • Convolutional Neural Networks (CNN) – are often used in computer sight and picture categorization applications, which can categorize the features and patterns within a picture, enabling object detection or acknowledgment. In an object recognition challenge conducted in 2015, CNN defeated and won a human in identifying objects.
  • Recurrent Neural Networks (RNN) – are often used in speech recognition and natural language applications, utilizing time-series data. 
  • Multi-Layer Perceptron (MLP) – is composed of a sequence of fully connected layers, which is very helpful in overcoming the requirement of high computing power. Architectural deep learning models require that.

Google’s Deep Learning machine learning program is accurate 89% of the time in detecting breast cancer, according to Google.

Real Examples of Deep Learning

Let us discuss the six fields where deep learning plays a vital role in technology: 

1. Virtual assistants 

Deep learning is a primary feature in human speech and language translation. Some advanced technological developments in virtual assistants are Siri, Cortona, and Alexa. 

2. Autonomous cars & vision for the driverless

Deep learning helps analyze the road scenarios, implications of different road signs, speed limits, working of signals, and pedestrians. 

3. Services & chatbots

Deep learning is implemented in continuous user interactions and answering tricky questions with an apt response.

4. Translations & facial recognition 

Deep learning supervision aids speech translation into multiple natural languages. Facial recognition characteristics using deep learning provide wider scope in security systems.

5. Shopping & entertainment 

The algorithms are rich in showing buyers suggestions by scanning the user’s history.

6. Pharmaceuticals

Deep learning has widened the scope of customization of medicines based on the particular genome and diseases. 

Applications of Deep Learning

  • Google Translator employs deep learning and image recognition to translate spoken and written languages. 
  • DCGAN handles the improvement and completion of human facial appearance. 
  • Online streaming platforms like Netflix, Amazon, and Spotify apply deep learning recommendation systems to list the best deals. 
  • PayPal, an online payment processor, utilizes deep learning to detect frauds. 
  • Google PlaNet manipulates photo analysis and determines the photo’s location through deep learning techniques.
  • DeepStereo operates in the conversion of street view photos into 3D space, which estimates each pixel’s depth and color and exhibits previously unseen images from various angles. 
  • DeepMind’s WaveNet is more advanced in creating naturally sounding speeches.
  • CamFind app eases the process of searching where you don’t have to type text anymore but take a picture of it.

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