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You might have been wondering what exactly this deep learning is. Wonder no more this week’s Cortouchmedia blog post is here to answer your questions on what and what deep learning is and what it entails. Come with me and let’s learn together.

Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier.

Deep learning examples

Because deep learning models process information in ways similar to the human brain, they can be applied to many tasks people do. Deep learning is currently used in most common image recognition tools, natural language processing (NLP) and speech recognition software. These tools are starting to appear in applications as diverse as self-driving cars and language translation services.

Use cases today for deep learning include all types of big data analytics applications, especially those focused on NLP, language translation, medical diagnosis, stock market trading signals, network security and image recognition.

Specific fields in which deep learning is currently being used include the following:

  • Customer experience (CX).Deep learning models are already being used for chatbots. And, as it continues to mature, deep learning is expected to be implemented in various businesses to improve CX and increase customer satisfaction.
  • Text generation.Machines are being taught the grammar and style of a piece of text and are then using this model to automatically create a completely new text matching the proper spelling, grammar and style of the original text.
  • Aerospace and military.Deep learning is being used to detect objects from satellites that identify areas of interest, as well as safe or unsafe zones for troops.
  • Industrial automation.Deep learning is improving worker safety in environments like factories and warehouses by providing services that automatically detect when a worker or object is getting too close to a machine.
  • Adding color.Color can be added to black-and-white photos and videos using deep learning models. In the past, this was an extremely time-consuming, manual process.
  • Medical research.Cancer researchers have started implementing deep learning into their practice as a way to automatically detect cancer cells.
  • Computer vision.Deep learning has greatly enhanced computer vision, providing computers with extreme accuracy for object detection and image classification, restoration and segmentation.

Limitations and challenges

The biggest limitation of deep learning models is they learn through observations. This means they only know what was in the data on which they trained. If a user has a small amount of data or it comes from one specific source that is not necessarily representative of the broader functional area, the models will not learn in a way that is generalizable.

The issue of biases is also a major problem for deep learning models. If a model trains on data that contains biases, the model will reproduce those biases in its predictions. This has been a vexing problem for deep learning programmers because models learn to differentiate based on subtle variations in data elements. Often, the factors it determines are important are not made explicitly clear to the programmer. This means, for example, a facial recognition model might make determinations about people’s characteristics based on things like race or gender without the programmer being aware.

The learning rate can also become a major challenge to deep learning models. If the rate is too high, then the model will converge too quickly, producing a less-than-optimal solution. If the rate is too low, then the process may get stuck, and it will be even harder to reach a solution.

The hardware requirements for deep learning models can also create limitations. Multicore high-performing graphics processing units (GPUs) and other similar processing units are required to ensure improved efficiency and decreased time consumption. However, these units are expensive and use large amounts of energy. Other hardware requirements include random access memory and a hard disk drive (HDD) or RAM-based solid-state drive (SSD).

Other limitations and challenges include the following:

  • Deep learning requires large amounts of data. Furthermore, the more powerful and accurate models will need more parameters, which, in turn, require more data.
  • Once trained, deep learning models become inflexible and cannot handle multitasking. They can deliver efficient and accurate solutions but only to one specific problem. Even solving a similar problem would require retraining the system.
  • Any application that requires reasoning — such as programming or applying the scientific method — long-term planning and algorithmlike data manipulation is completely beyond what current deep learning techniques can do, even with large data.

Deep learning vs. machine learning

Deep learning is a subset of machine learning that differentiates itself through the way it solves problems. Machine learning requires a domain expert to identify most applied features. On the other hand, deep learning understands features incrementally, thus eliminating the need for domain expertise. This makes deep learning algorithms take much longer to train than machine learning algorithms, which only need a few seconds to a few hours. However, the reverse is true during testing. Deep learning algorithms take much less time to run tests than machine learning algorithms, whose test time increases along with the size of the data.

Furthermore, machine learning does not require the same costly, high-end machines and high-performing GPUs that deep learning does.

In the end, many data scientists choose traditional machine learning over deep learning due to its superior interpretability, or the ability to make sense of the solutions. Machine learning algorithms are also preferred when the data is small.

Instances where deep learning becomes preferable include situations where there is a large amount of data, a lack of domain understanding for feature introspection, or complex problems, such as speech recognition and NLP.

Conclusively, deep learning can be opened up to you more as you come and learn with us in Cortouchmedia  . In Cortouchmedia , we take you through the step by step of  artificial intelligence  , Machine learning, Data Science e.t.c that can open up the know-how of deep learning. Come on board with us and you will be happy you did.

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