The following diagram shows you one such application where you feed an animal image to a neural network and it tells you that the image is of a dog. You give it a certain input and it will provide you a specific output. Black Box approachĪn ANN is like a blackbox. We will now study each one of these limitations in detail. Some of the important points that you need to consider before using deep learning are listed below − Now, we will look at some of the limitations of deep learning that we must consider before using it in our machine learning application. Due to this, the development of the deep learning applications that we mentioned above became a reality today and in the future too we can see the applications in those untapped areas that we discussed earlier. Fortunately, today we have an easy availability of HPC – High Performance Computing. You need both memory as well as the CPU to develop deep learning models. To use deep learning, supercomputing power is a mandatory requirement. What is Required for Achieving More Using Deep Learning The possibilities are endless and one has to keep watching as the new ideas and developments pop up frequently. Some of these are discussed here.Īgriculture is one such industry where people can apply deep learning techniques to improve the crop yield.Ĭonsumer finance is another area where machine learning can greatly help in providing early detection on frauds and analyzing customer’s ability to pay.ĭeep learning techniques are also applied to the field of medicine to create new drugs and provide a personalized prescription to a patient. There are several domains in which deep learning techniques are successfully applied and there are many other domains which can be exploited.
Untapped Opportunities of Deep LearningĪfter looking at the great success deep learning applications have achieved in many domains, people started exploring other domains where machine learning was not so far applied. Face detection, face ID, face tagging, identifying objects in an image – all these use deep learning. Mobile Apps − We use several web-based and mobile apps for organizing our photos. Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortena and Google’s Assistant – all these use deep learning techniques. All of us use several mobile apps today that are capable of recognizing our speech. Speech Recognition − Another interesting application of Deep Learning is speech recognition. They generally adapt to the ever changing traffic situations and get better and better at driving over a period of time. Self-driving Cars − The autonomous self-driving cars use deep learning techniques. Applicationsĭeep Learning has shown a lot of success in several areas of machine learning applications. First we will look at a few deep learning applications that will give you an idea of its power.
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