Deep learning is a machine learning technique that teaches the computer to perform what naturally comes to humans. This form of technology can evidently be seen on driverless cars. It helps them in recognising a stop sign or to differentiate a pedestrian from a lamppost. Deep Learning is also the key in successfully controlling the voice aspect of devices such as our phones, tablets, TVs, and hands-free speakers. This has been garnering loads of attention over some time now. In deep learning, a computer model is made to learn so that he is able to perform classified tasks directly from images, text, or sound patterns. Through this, the technology is capable of reaching great accuracy that can sometimes even surpass the human level. Models are trained by using a large set of labelled data and neural network architectures that contain various forms of layers.
Deep Learning is known to achieve great levels of accuracy at higher levels, more than ever before. Majorly this form of technology has been helping in meeting the soaring demands and expectations of consumers. It is also becoming an extremely crucial factor in safety-critical applications like driverless cars. Its advancement has increased to such a point that it has been successfully outperforming humans in some tasks like classifying objects in images. But what are the main reasons behind the usability of Deep Learning which makes it so popular?
1. Deep learning technology requires large amounts of labelled data. For example, the development of the driverless car that excites millions of people around actually requires millions of images and thousands of hours of videos.
2. Deep learning requires a substantial amount of computing power. The high-performing GPUs have a parallel form of architecture that is considered extremely efficient for deep learning technology. When it is combined with clusters or cloud computing, it enables development teams to reduce the training time for a deep learning network from the span of weeks to a couple of hours or even less.
Where do we see Deep Learning?
This form of advance technology has been impacting in numerous industries. One of the most prominent places were we get to spot Deep Learning is the Automative research industry. This technology can be easily detected in objects such as stop signs and traffic lights. It is also used to help spot pedestrians that further help in decreasing the rate of accidents.
Deep learning is also used to identify objects from satellites that may or may not be in the areas of interest. This also helps in identifying safe or unsafe zones or paths for the troops.
It had been known that many cancer researchers are using deep learning technology to easily detect cancer cells in the human body. Various teams at the University of California in Los Angeles have successfully built an advanced microscope that yields a high-dimensional data set which has been used to train a deep learning application to accurately identify cancer cells.
In the Industrial Automation industry, Deep learning has been helping in improving the aspects of safety of the workers that work around heavy machinery on a daily basis. The technology automatically detects when someone or an object is within an unsafe parameter of the machines.
As for the electronic industry, Deep learning is being used in automated hearing and speech translation. This can be evidently seen in-home assistance devices that respond to your voices and know your preferences are powered by deep learning applications.
How Deep Learning Works?
The deep learning technology uses neural network architectures. This is also the reason why deep learning models are also referred to as deep neural networks. Whereas the term’deep’ refers to the number of hidden layers in the neural network. Traditional neural networks only contain 2-3 hidden layers. But for deep networks, you can have as many as 150 layers. The deep learning models are trained by using large sets of labelled data and neural network architectures. This learning features directly from the data without the need for manual feature extraction. One of the most popular neural networks for deep learning which is known as convolutional neural networks. This form of the neural network involves learned features with input data that uses 2D convolutional layers. This helps in making the architecture well suited to processing 2D data, such as images. The convolutional neural network helps in eliminating the need for manual feature extraction. Therefore, you don’t need to identify features used to classify images. It worked in extracting features directly from images. These relevant features are not pretrained. They are learned while the network trains on a collection of images. This automated feature extraction makes deep learning models highly accurate for computer vision tasks such as object classification. They learn to detect different features of an image using tens or hundreds of hidden layers. Under these layers, the ones that are found to be hidden manage to increase the complexity of the image features. The first hidden layer is able to detect the edges, and the last learn how to detect more complex shapes. This specifically catered to the shape of the object we are trying to recognize.
Difference between Machine Learning and Deep Learning
Deep Learning is a form of specialised machine learning. The machine learning process involves relevant features being extracted from images manually. The features are then further used to create a model that helps in classifying the objects in the image. In a deep learning workflow, the relevant and appropriate features are automatically extracted from images. Deep learning also works around end-to-end learning. When a network is given raw data and a task to perform, such as classification, it learns how to do this automatically.
Advantages of using Deep Learning
1. Research has shown that many organization’s have data that is unstructured. This is the majority due to its existence in different types of formats like pictures or texts. A major part of the machine learning algorithms is unable to analyze the unstructured data. This means that the remaining becomes unutilized. Hence, this is where deep learning acts useful. This helps in utilizing different data formats to train deep learning algorithms and obtain insights which are relevant to the purpose of the training.
2. One of the biggest advantages of using deep learning is its ability to execute feature engineering by itself. In this, an algorithm scans the data to identify features which further correlate and combine them to promote faster learning without being commanded to do so explicitly. This helps save the time and effort of many data scientists.
3. Once trained properly, a deep learning model can perform thousands of routine, repetitive tasks in a shorter period of time as compared to a human being’s effort. Also, the quality of the work never degenerates, unless the training data contains raw data which doesn’t represent the problem you’re trying to solve.
4. With the help of deep learning, subjective defects can be detected. Deep learning models are also capable of identifying defects which would be difficult to detect usually. When consistent images become challenging, deep learning can help in accounting for those variations and learn valuable features to make the inspections robust.