What is Deep Learning?
Deep learning is a subset of machine learning which teaches machines to do what humans are naturally born with: learn by example. Though the technology is often considered a set of algorithms which mimics the brain, a more appropriate description would be a set of algorithms which ‘learns in layers’. It involves learning through layers that enable a computer to develop a hierarchy of complicated concepts from simpler concepts. Deep learning is the central technology behind a lot of high-end innovations like driverless cars, voice control in devices like tablets, smart phones, hands-free speakers etc. and many more. It’s offering results which were not possible before or even with traditional machine learning techniques.
A large number of businesses are using deep learning to leverage its benefits. Let’s have a look on them.
- Electronics: Deep learning is being utilized in automated speech translation. You can think of home assistance devices which respond to your voice and understand your preferences.
- Automated Driving: With the help of deep learning, automotive researchers are now able to detect objects like traffic lights, stop signs etc. automatically. They are also using it to detect pedestrians that help lower accidents.
- Medical Research: Deep learning is being used by cancer researchers to detect cancer cells automatically.
How Deep Learning Model Works?
Majority of the deep learning methods utilize neural network architectures and that’s why deep learning models are widely known as deep neural networks as well. A deep learning process consists of two key phases, training and inferring. The training phase can be considered as a process of labelling huge amounts of data and identifying their matching characteristics. Here, the system compares those characteristics and memorizes them to come up with correct conclusions when it encounters similar data next time. During the inferring phase, the model makes conclusions and labels unexposed data with the help of the knowledge it gained previously. During the training of deep learning models, professionals use large sets of labelled data together with neural network architectures which learn features from the data directly without the need for feature extraction done manually.
How Deep Learning Models Are Developed & Trained?
- Transfer learning: The transfer learning approach is being used by most deep learning, which involves fine-tuning a pre-trained model. For instance, you begin with an existing network and feed in fresh data that contains previously unknown classes. After doing some modifications to the network, you become able to perform a new task like categorizing only cats or dogs rather than 1000 different objects. This approach also comes with the advantage of requiring much less data, so computation time drops significantly.
- Training from scratch: In order to train a deep learningnetwork from scratch, you’d need to capture a very large labelled dataset apart from designing a network architecture which will learn the features and mimic. This approach is effective for new applications, or for applications which will have a relatively big number of output categories. This is a relatively less popular approach because, with the rate of learning and large volumes of data, the networks typically take significantly more time to train.
- Feature extraction: It’s a more specialized, slightly less common approach to deep learning where the network is used as a feature extractor. Here, all the layers are assigned to learn specific features from images and thus, during the training process, these features can be pulled out of the network at any time. Then these features can be utilized as input to machine learning
Key Benefits of Using Deep Learning
A significant number of technology giants are steadily adopting deep learning. To understand the reason, we have to look into the advantages that can be gained by using a deep learning approach. Here are some of the key advantages of using this technology.
Helps To Improve Unstructured Data
According to experts survey, it is revealed that a huge percentage of an organization’s data is unstructured because the majority of it exists in different types of formats like pictures, texts etc. For the majority of machine learning algorithms, it is difficult to analyze unstructured data, which means it is remaining unutilized and this is exactly where deep learning becomes useful. You can use different data formats to train deep learning algorithms and still obtain insights which are relevant to the purpose of the training.
Eliminates Featured Engineering Needs
In machine learning process, feature engineering is a fundamental job as it improves accuracy and sometimes the process can require domain knowledge about a certain problem. One of the biggest advantages of using deep learning approach is its ability to execute feature engineering by itself. In this approach, an algorithm scans the data to identify features which correlate and then combine them to promote faster learning without being told to do so explicitly. This ability helps data scientists to save a significant amount of work.
Capability to Deliver High Quality Results
Humans get hungry or tired and sometimes make careless mistakes. When it comes to neural networks, this isn’t the case. Once trained properly, a deep learning model becomes able to perform thousands of routine, repetitive tasks within a relatively shorter period of time compared to what it would take for a human being. In addition, the quality of the work never degrades, unless the training data contains raw data which doesn’t represent the problem you’re trying to solve.
Helps to Cut Off Unnecessary Costs
Recall functionalities are highly expensive and for some industries, a recall can cost an organization millions of dollars in direct costs. With the help of deep learning, subjective defects which are hard to train like minor product labelling errors etc. can be detected. When consistent images become challenging because of different reasons, deep learning can account for those variations and learn valuable features to make the inspections robust.
No Need of Data Labelling
Data labelling can be an expensive and time-consuming job. With a deep learning approach, the need for well-labelled data becomes obsolete as the algorithms excel at learning without any guideline. Other types of machine learning approaches aren’t nearly as successful as this type of learning.
Deep learning has come a long way from being just a trend and it’s quickly becoming a very popular technology being adopted steadily by large scale businesses & various industries.
