Deep learning is a set of machine learning algorithms that model high-level abstractions in data using architectures consisting of multiple nonlinear transformations. What does it mean?
How Deep Learning Works
A deep machine learning process consists of two main phases: training and inferring. You should think about the training phase as a process of labeling large amounts of data and determining their matching characteristics. The system compares these characteristics and memorizes them to make correct conclusions when it faces similar data next time.
A deep learning training process includes following stages:
- ANNs ask a set of binary false/true questions or.
- Extracting numerical values from data blocks.
- Classifying data according to the answers received.
- Labeling Data.
During the inferring phase, the deep learning AI makes conclusions and label new unexposed data using their previous knowledge.
Advantages of Deep Learning
In 2016, Grand View Research (GVR) estimated the global deep learning market in $272 million. Its significant part (20%) belonged to both aerospace and defense industries. From 2014, the deep learning market shows a continuous parabolic growth. GVR’s latest report states that this market will reach the value of $10.2 billion by the end of 2025. So what did cause such a remarkable market growth? The answer lies in the set of advantages provided by a deep learning technology.
Creating New Features
One of the main benefits of deep learning over various machine learning algorithms is its ability to generate new features from limited series of features located in the training dataset. Therefore, deep learning algorithms can create new tasks to solve current ones. What does it mean for data scientists working in technological startups?
Since deep learning can create features without a human intervention, data scientists can save much time on working with big data and relying on this technology. It allows them to use more complex sets of features in comparison with traditional machine learning software.
Due to its improved data processing models, deep learning generates actionable results when solving data science tasks. While machine learning works only with labeled data, deep learning supports unsupervised learning techniques that allow the system become smarter on its own. The capacity to determine the most important features allows deep learning to efficiently provide data scientists with concise and reliable analysis results.
Deep Learning Challenges
Deep learning is an approach that models human abstract thinking (or at least represents an attempt to approach it) rather than using it. However, this technology has a set of significant disadvantages despite all its benefits.
Continuous Input Data Management
In deep learning, a training process is based on analyzing large amounts of data. Although, fast-moving and streaming input data provides little time for ensuring an efficient training process. That is why data scientists have to adapt their deep learning algorithms in the way neural networks can handle large amounts of continuous input data.
Ensuring Conclusion Transparency
Another important disadvantage of deep learning software is that it is incapable of providing arguments why it has reached a certain conclusion. Unlike in case of traditional machine learning, you cannot follow an algorithm to find out why your system has decided that it is a cat on a picture, not a dog. To correct errors in DL algorithms, you have to revise the whole algorithm.
Deep learning is a quite resource-demanding technology. It requires more powerful GPUs, high-performance graphics processing units, large amounts of storage to train the models, etc. Furthermore, this technology needs more time to train in comparison with traditional machine learning.
Despite all its challenges, deep learning discovers new improved methods of unstructured big data analytics for those with the intention to use it. Indeed, businesses can gain significant benefits from using deep learning within their tasks of data processing. Though, the question is not whether this technology is useful, rather how companies can implement it in their projects to improve the way they process data.
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