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Machine learning trends in 2021: Top trending machine learning technologies to drive AI/ML market in 2021

Machine learning is going to revolutionized the industries in the coming years, in 2020 we have seen tremendous growth in the Machine learning and AI technologies. In 2021 machine learning will drive many business including medicine, health, E-commerce, agriculture and others. Here we are going to present you the machine learning trends for 2021 that will shape the industries in this year.

As we already know that Deep learning has become one of the hottest buzzwords in the computer science world. We used machine learning for image classification, image annotation, image recommendation, text classification and many others use cases.

Machine Learning and Artificial intelligence is being used extensively in the industry, as per one estimate over 77% of devices that we presently using is powered with ML/AI in one or other way. So, the use of ML/AI is growing fast and in 2021 we will see steep growth in the use of Machine Learning.

Today, there are several important breakthroughs that bring machine learning closer to real-world applications than ever before. Industries are researching on AI/ML technologies to develop solution to the real-world problem and then integrating them into a solution that can be used by their client.  These developments have been achieved by the massive amount of data collected by artificial intelligence systems. However, these systems are currently limited to certain tasks (e.g. speech recognition, image and video recognition, language translation, etc.) and are still not capable of working on the most complex issues, but research on these complex problems is being done by Data Scientists around the world.

The deep reinforcement learning is also playing a major role in ML/AI projects. New developments in deep reinforcement learning and deep learning algorithms have shown that the capabilities of reinforcement learning are much higher. These algorithms can be trained on large data sets,  which helps increase the accuracy of their predictions. For example, they have been trained to learn from image recognition, from videos from a live event, and from a real-time web camera. Deep reinforcement learning models have demonstrated impressive progress in these areas. It is expected that they will improve the quality of decision making in systems like those mentioned above, such as driverless taxis, intelligent machines (such as robots or medical diagnostics), or machine learning applications in general environments. So, the use of deep reinforcement learning will high in coming years including 2021 and 2022. Data Scientists and Machine Learning professionals must learn deep reinforcement learning techniques in 2021 and beyond.

Deep reinforcement learning models are able to perform complex calculations without requiring any information from the user. For example, the first deep reinforcement learning models were able to recognize handwritten characters and distinguish human from animal. They also successfully identify small differences in noise in human speech, even when the background noise was low. The same approach works well when working with real-time images.

A major leap forward in modern AI is the advancement of deep reinforcement learning techniques. Even human-level AI cannot perform tasks as simple or as reliable as machine learning can. These advances allow systems to learn from huge amounts of information, making them faster and more accurate. Moreover, they can be programmed to perform difficult tasks without requiring any knowledge of the user’s previous experiences, which is a major step forward on the way we are learning to interact with machines.

The Deep reinforcement learning (deep RL) is very powerful technique which combines reinforcement learning and deep learning. The deep reinforcement learning algorithm is a special version of reinforcement learning algorithms in combination with deep learning, which is designed to find optimal solutions to problems without a full understanding of the input data. Deep learning employs agents to make decision from unstructured data input and all works with manual engineering of the state. In this way, new AI systems can learn from a range of very similar problems in a relatively short amount of time. The basic idea behind deep reinforcement learning is to use a probabilistic approach by generating a set of possible problems with different weights and then “playing” with the algorithms in a search for the parameters of a solution. In the framework of the above mentioned probabilistic approach, each time a method for solving one problem is asked, its probability is updated, based on the previous values, and the final solution is based on the combination of the previous weights.

Deep reinforcement learning is used in many fields including robotics, video games, natural language processing, computer vision, education, transportation, finance and healthcare.  There are unlimited uses of deep reinforcement learning in various fields today. The use of deep reinforcement learning will grow fast in 2021 and beyond.

These developments represent not only a step forward for AI, but many other important advancements. It is expected that in the future, these advances will eventually help create more accurate driverless cars, which are essential for the mass adoption of driverless vehicles. Meanwhile, algorithms based on deep reinforcement learning have contributed significantly to image recognition using Deep ImageNet, the largest of its kind, also known as an artificial neural network (ANN). By using deep reinforcement learning techniques, these systems learned to reliably learn from images that had been manipulated for different purposes. As this was achieved in real-time, these systems were able to recognize the subject matter (e.g. vehicles, faces) of faces. So, the use of deep reinforcement learning will see fast growth in coming years.

The next major breakthrough in machine learning is the advent of reinforcement learning, which is a system for learning from a limited set of tasks. A simple demonstration of the capability comes from the task of handwriting recognition. The first step involved training an algorithm to find an effective solution for detecting handwritten digits; for this, the algorithms learned to read handwritten handwriting from images, which in turn were manipulated for different purposes. This technique is called deep handwritten recognition. Deep handwritten recognition has been applied to recognize handwritten images in high-dimension images, such as landscapes, which is used in several applications like real-time natural language applications, real-time 3D models, and 3D animation. So, we can say that there are large number of usage of deep reinforcement learning in the machine learning and artificial intelligence field. If you are in this field then it will give you better career prospects if you learn deep reinforcement leaning in 2021.

Top Machine learning trends in 2021

We are now discussing the top machine learning technologies that will drive AI/ML industry in 2021.

  1. Reinforcement Learning

Reinforcement learning is subset of machine learning which employs the actors while training the model. In this type of learning actor awards if the action taken is correct. For example in a game application if the player takes right move then award is given else game might give me minus point. This way during training model learns all the correct actions.

The reinforcement learning is becoming very popular in training various types of models and it is used robotics, driver-less car etc. The demand for Data Scientists skilled in enforcement learning will increase in 2021.

  1. Deep reinforcement learning

The Deep reinforcement learning is the combination of reinforcement learning and machine learning as explained above in this post. Professional having experience in deep reinforcement learning will find year 2021 much better as they will get more opportunities in coming months.

  1. Machine Learning In Hyperautomation

The Hyperautomation is advanced machine learning technologies that uses top technoloiges such as artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) to automate the tasks that usually done by humans. The use of Hyperautomation is increasing fast as more and more institutions are researching on this. This technology will be in high demand in 2021 and Data Scientists much spend time to learn it.

  1. Use of AI/ML in Business Forecasting and Analysis

The use of AI/ML in business is increasing fast and in year 2021 more business will start using machine learning technologies to power their business. The most uses of AI/ML in business are forecasting, predictive analytics and analysis of vast amount of data at very fast speed. Business will also use cloud computing environment for large scale processing of data instead of setting-up own server in-house.  Data Scientists should learn technologies used for business forecasting and business data analytics for better job prospects.

  1. Automation

The AI/ML technologies is very useful in analyzing vast amount of business and it analytics generated by machine learning programs add great business value. In the year 2021 business will have to use the automation using AI/ML for data predictions to run their business or they may left behind in the race of fast automation. So, most of the business will adopt fast automation technologies in their day to day business.

  1. Vast use of ML in IoT devices

Due to availability of ML technologies and appropriate computing hardware for the IoT devices, the use of ML in Edge devices will grow fast. Most of the companies are researching on the use of ML in IoT devices and in coming years we will see many ML enabled IoT devices. Same this is happening in cast IIoT, where trained ML algorithms are being used on the Edge devices to solve various industry problems. This way AI/ML technologies will power the IoT and IIoT devices in near future. In the year 2021 we will see the fast growth in ML enable IoT devices market.

  1. Faster Computing Power

Today’s  machine learning and artificial intelligence application demands fast computing devices to run computation while running large scale model training. The complexity increases when we have to train deep learning model with tons of data and in this case fast computing device is needed. To meet the expectations of the Data Scientists hardware manufacturers are coming up with fast computing servers supporting multiple GPUs. So, in the year 2021 there will be huge demand of fast computing servers to train model on large scale with huge amount of data.

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