Deep learning is an advanced form of machine learning that uses many layers of neural networks to achieve accuracy in predicting results. Neural networks enable a computer to learn from data. Building algorithms using different techniques of neural networks is the main purpose of deep learning. In the human brain, neural networks are made by a set of neurons. From these networks, deep learning is inspired. It trains computers to do what comes naturally to a human’s mind. Deep learning algorithms receive input, then extract features from input data by processing it from different layers of neural networks. Deep learning has no need for manual feature extraction. After processing through all layers, this model predicts the given objects and gives the output.
Deep learning applications are used across all industries from automated driving to medical devices:
- Deep learning is playing a crucial role in Automated Driving. It is used to detect objects such as traffic lights and stop signs. It is also used to distinguish a pedestrian from a lamppost which helps reduce accidents.
- In consumer electronics like phones, TVs, and tablets, it is the key to voice control.
- It is vital for the medical research industry. Cancer researchers are using it to automatically detect cancer cells. It is reshaping the healthcare industry by delivering new possibilities to improve people’s life.
- It is used in Entertainment Industry, such as Netflix, Amazon, and Film Making.
- It is heavily used these days in building robots to perform human-like tasks.
- It is used in Industrial Automation to improve worker safety around heavy machinery. It detects when people are within an unsafe distance from that machinery.
Difference between Machine learning and Deep learning:
To understand the main difference between them let us take a look at an example:
We have a set of images of dogs and cats. Now, we want to identify both objects separately by using machine learning algorithms and deep neural networks.
- Using Machine learning algorithms: Machine learning needs structured data. To have a computer do classification using this approach, we would manually select the relevant features of an image such as edges or corners in order to train the machine learning model. The model will use these features information to classify new objects.
- Using deep learning neural networks: To classify both animals, the neural networks model relies on the outputs given by each network. Input data comes through different layers of this network. Each layer hierarchically finds features of the images. This “Multiple Layer Processing” is useful because lower-layers may identify edges of the images and higher-layers can identify human concepts such as faces or letters. At the point when information is prepared from all levels, the system finds appropriate identifiers to classify both animals by their images.
You can also see the difference between both techniques in the below image:
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