Firms can save time and cost with Deep learning methodologies!

The rise in Machine Learning, Deep Learning and Artificial Intelligence technologies seems to breaking all barriers. All these technologies have the potential to spur innovation everywhere in the world. When it comes to deep learning, the technological advancement seem to be very uplifting. They have the power to help the companies perform better and more quickly. But, at the same time, there is certain amount of confusion surrounding these technologies as well. However, these muddles never negate the superb potential of AI, ML or deep learning. One of the major confusion that persists in the society is the connection between deep learning and big data analytics. Are they similar? We will find out.

How is deep learning different from big data analytics?

Big Data Analytics and Deep Learning are not at all exactly the same. But, they aren’t two absolutely diverse concepts as well. Well, in most of the situations, Big Data is mostly used in deep learning. However, there isn’t any mandate connection or correlation between the two. When it comes to Big Data Analytics, the main aim of doing it is to generate a plenty of useful insights from the voluminous quantity of data. Large chunks of data are easily exposable to the businesses, and only by segregating and processing the data with the help of big data tactics, you can get to the insights you require. When it comes to deep learning, it is much more related to Artificial Intelligence. Deep learning is a part of machine learning.

How is deep learning even useful?

Deep learning is useful in numerous ways than one. It is one of the most evolved parts of machine learning. The technology basically deals with understanding the data representations. And, these representations might be either supervised or unsupervised. However, deep learning isn’t associated with task-specific algorithms. This is what makes it different from all the other machine learning methodologies. All the data representations in deep learning are roughly centered on the data processing and communication patterns.

  • Deep Learning is a specific, very advanced technique in Machine Learning.
  • In deep learning, each of the distinctive node in the network signifies a “feature” that helps in representing any input or allocating a class to that input.
  • Deep learning makes use of Artificial Neural Network with a plenty of layers.
  • It uses smart methods to learn the ideal model parameters.
  • Importantly, the people developing the network do not have to put any of the features in themselves.
  • The deep learning algorithm are smart enough to choose which features should be used to complete the task.

Why should businesses use deep learning?

Deep learning has the potential to benefit businesses in a great way! Starting from owning a robotic process automation which would conveniently managing the customer support teams. In fact, they would take over the chats, and take in more information and also give out better answers than only scripted answers. The advanced solution will keep learning to answer not only better, but a lot more quickly as well. At the end of the day, it is going to definitely benefit both the customer and the company. Without employee overhead burden, the company will get a chance to grow enormously.

Intelligent solutions have the power to positively influence the working of a firm. When a firm thinks of progressing, the smart automated solution which consists of deep learning is exactly something that a firm wants! This is exactly where the deep learning based solutions will help to save time and cost! They will do whatever the human workers were supposed to do, but at a greater speed and a much improved efficiency. Also, the cost of hiring numerous resources will go down!

Deep learning has been used since quite a few years now, but the impact can be felt now, and the future seems to be brighter. However, when we talk about the initial stage of deep learning, it is known widely for the powerful image classification models. All of such models were capable of learning objects in the images a lot more efficiently. It’s just that the experts have to carefully train them to differentiate between the real images from counterfeit images.

However, that was just the beginning. Now, ‘Neural networks’ can be referred as the perfectly trainable ‘brains’ for your business. All you have to do is, just feed in the required information, and teach them how to do an activity. They will automatically make use of the training you have along with some new info and will also learn from their own experiences to deliver better and better. Therefore, they are not just fast, but extremely cost effective as well.

Deep learning is revamping the designing space

Deep learning is already helping the designers to make a new edition of a product. With the help of data from the sensors and some of the finest deep learning methods, the experts are able to optimize the designs. We have already seen that deep learning, AI, ML and big data have augmented creativity the design space. The biggest example is the IBM’s machine learning system, Watson. A large number of images were inserted into the system, and of the images were the work of artist Gaudi. Also, there was some complementary material added as well. This was done to basically enable the machine learn conceivable impacts for the great artist’s work. Then, Watson studied all the data, and sent out the inspiration to the human artists. The information was specifically sent to the artists who were charged with making an art piece that was ‘informed’ by Watson. However, the sculpture did contain the style of Gaudi.


Deep learning is everywhere. Most of the big firms have started using it already, but surprisingly, even the small firms aren’t left out in the cold. They are also trying their best to use it to boost their performance. Specifically because of the cloud, every firm has the option to invest in machine learning. So, ML and specifically deep learning has the potential to allow the firms improve their productivity, efficiency as well as the business processes.


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