20 Deep Learning Applications

In many companies, the use of machine learning and deep learning techniques is going to be widely used. They can be used in the medical field to help us learn more about different diseases and to help a doctor provide an accurate diagnosis in a faster manner.

They can be used in the retail industry to help figure out the best marketing techniques to reach customers and where to place different products. Manufacturing firms can use deep learning to help them find where waste is to eliminate it and to make predictions on when a piece of equipment is going to fail, so they can fix it ahead of time. Related article: TensorFlow Library for Deep Learning

The Applications of Deep Learning:

Even with all of these options, there are still some other targeted approaches and uses of machine learning, especially the kind that have been assisted with deep learning. Some of these are going to include:
  1. Text analysis: This is going to be a form of predictive analytics that can help us extract the sentiments behind the text, based on the intensity of the presses on the keys, and the typing speeds.
  2. Artificial intelligence: This may be its own field, but it can take a lot of its cues from deep learning. This is because many of the artificial intelligence models that you work with are going to put the idea of neural networks to use. Google DeepMind is a good example of this.
  3. Predictive anomaly analysis: Deep learning can help us to find out when there are some anomalies and abnormal patterns in signals. This can be useful for many companies who want to catch catastrophes early on and can help us to avoid some failures on major systems.
    • The deep neural networks are going to be preferred in many cases when it is time to detect these anomalies because it is able to take the input signal and reconstruct it as the output. If there are any changes that happen with the journey, this will be reported. If you have a network that goes really deep, it is possible to work with this information in a manner that is more refined.
  4. Failure analysis: Neural networks are often able to detect failures that will happen, ahead of time, even when they aren’t meant for the failing system. The server overloads and some other behaviors that may be erratic are also easily detected if we set up deep learning.
  5. Disruptions in IT environments: Most of the organizational services of seen some big changes over the years, including things like microservices, delivery systems, and IoT’s. While most teams are working on a variety of tools to help comprehend the nature of these services, deep learning is going to be helpful when it is time to gauge the patterns and then change up the IT stacks.

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