Top 5 deep learning trends of 2019

The area of artificial intelligence (AI), has been flourishing its roots to almost every field. Along with that, we are hearing new technologies and their advancements in the modernization of our future and current lifestyle.

Deep learning and deep neural networks are such technological advancement surrounding AI now steering the current course in every sector. Deep learning is a machine learning (ML) technique that tries to extract high-level abstract data representations through hierarchically combining simple features into more complex features layer by layer.

Deep learning promises a powerful, and fast machine learning process. As a result, it is the growing trend in ML due to some favorable results in applications where the target function is very complex and the datasets are large.

Deep learning teaches computers to do what comes naturally to humans i.e., learn by example. Deep learning is a key technology behind driverless cars, helping them to distinguish a pedestrian from a tree, recognize a stop sign. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.

In the coming years, we are going to see the clear domination of deep learning in every field. Here are the top 5 deep learning trends of 2019.

Top deep learning trends:

1. Cloud adoption

For improve the scalability of Internet-based database capabilities while reducing cost and risk organizations are utilizing the power of cloud hosting. Earlier machine learning systems were too costly and too complex for most seekers but today cloud is changing all that.

Today we have numerous option to place these heavy systems in the cloud. For example, Amazon Web Services supports machine learning using AWS’s algorithms to read native AWS data (such as RDS, Redshift, and S3). Like that, Google has supported predictive analysts for some time with its Google Prediction API and also Microsoft provides an Azure machine-learning service.

2. Improved AI assistants integrated with IoT

Now we have the capability to control IoT home systems with our mobile devices. Every giant AI assistants like Microsoft Cortana, Google Assistant, and Amazon Alexa, all are now following the developing IoT rage resulting in turning into a standard in homes to mechanize particular jobs.

3. Capsule neural networks

Capsule neural network is a growing form deep neural network. More commonly known as CapsNet, is a type of artificial neural network that can be used to better model hierarchical relationships. It process information like the human brain. Unlike the convolutional neural network, capsule neural network retrieves a higher level of accuracy with a minimal number of errors.

4. Video-based activity recognition

Video-based human activity recognition is one of the improving areas under research today for intelligent video surveillance. With the integration of deep learning on video analysis can accomplish the task of activity recognition. Deep neural networks can dramatically improve recognition performance because of its hierarchical nature to exploit the video frame structure in reducing the search space of the learning model.

5. Geospatial applications

Many geospatial tasks, like automatic feature extraction, have proven to be challenging and difficult to get exact results because feature characteristics varying widely from place to place. A deterministic algorithm that performs well in one region may well fail miserably in another. For example, it may be hard for it to distinguish between rooftops across a large dataset. Because it is always learning, a deep learning algorithm can overcome such limitations and train itself to recognize rooftops across the entire region.

Deep learning can take most of the heavy lifting of the human’s shoulders. AI is not dangerous — these algorithms are nothing to fear — at least not yet.

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