The Future of Deep Learning

When thinking of technology, one cannot go without talking about deep learning. Needless to say, deep learning has become one of the most critical aspects of technology. Gone are the days when organizations alone used to show interest in technologies like AI, deep learning, machine learning, etc. Today, even individuals are inclined towards the very aspect of technology, deep learning in particular. One of the many reasons why deep learning draws all the attention is because of its ability to enable improved data-driven decisions and also improve the accuracy of the predictions made.

In a nutshell, companies are in a position to reap out various financial and operational benefits by virtue of deep learning. With many deep learning innovations proliferating with time, it makes every possible sense to have a clear picture as to how does the future of deep learning looks like. In line with what we have seen over the past few years, this is what we could expect in the coming days as far as deep learning is concerned –

• Despite the fact that deep learning is a little on the slower side when compared to traditional AI and other machine learning algorithms, what one can stay assured of is the fact that it is way more powerful as well as straightforward. It is because of this that fields such as medicine, supply chain, robotics, manufacturing, etc. would see immense usage of deep learning in the days that lie ahead.

• A few years from now, it is very much possible that deep learning development tools, libraries, and languages could become standard components of every software development tool kit. These tool kits with modern capabilities will pave the way for easy design, configuration, and training of new models. With these capabilities, style transformation, auto-tagging, music composition, etc. would be a lot easier to accomplish.

• The need for faster coding is at an all-time high. The future is all set to see the deep learning developers adopting integrated, open, cloud-based development environments that provide access to a wide range of off-the-shelf and pluggable algorithm libraries.

• The prediction that neural architecture search would play a pivotal role in building data sets for the deep learning models still stands strong.

• Global marketers have a positive mindset by virtue of Google's acquisition of DeepMind Technologies.

• It is highly likely that the deep learning networks would demystify computer memory.

• Yet another point that is worth making a note of is the fact that the automation of deep learning tools would mean that there's an inherent risk that could develop into something so complex that the average developers will find themselves totally ignorant.

• Deep learning should be able to demonstrate learning from limited training materials and transfer learning between contexts, continuous learning, and adaptive capabilities. Wondering why. Well, just to remain useful.

What everything boils down to is the fact that as a result of the growing popularity of deep learning and with the advancement in technology, by the end of this decade, the deep learning industry will simplify its offerings considerably so that they're comprehensible and useful to the average developer.