Discriminate between deep learning and deep q learning

Technology is redefining our lives in different ways; from the way we use our mobile phones to driving the car, everything has a technological intervention. In recent years we have seen machines working like humans and trying to comprehend and work as a human. All this is a blessing of technology, and it continues to grow with time. The technology we will focus on in this blog is deep learning and deep Q learning; both are an integral part of machine learning.

Deep learning is a subset of machine learning and is based on artificial neural networks. Deep learning is based on artificial neural networks. The neural network mimics the human brain, so deep learning also mimics the human brain. Here we would like to highlight that the concept of deep learning is not new, it has been in existence for a long time, but it's the only recently that it has got so much hype. Moreover, with higher processing power and the availability of a lot of data, there is more data processing.

Many people misinterpret machine learning with deep learning, but deep learning deals with a large volume of data. While the world is discussing so much about machine learning and deep learning, there are other concepts associated with deep learning that are emerging. We are talking about deep Q learning and reinforcement learning. By now, you know that deep learning works on large data sets using the high-end machine for better and faster computing. Coming to deep Q learning, then it is a part of reinforcement learning.

What is deep reinforcement learning?

To better understand the concepts of deep Q learning, one needs to delve a bit deeper into deep reinforcement learning. It blends an artificial neural network with a reinforcement learning architecture, enabling the software-defined agents to learn the best action in a virtual environment.

The core of machine learning, deep learning, and AI is a neural network that functions similarly to the human brain. When we combine this neural network with a reinforcement learning algorithm, it helps in creating some amazing algorithms like Deepmind’s AlphaGo. The most astonishing part of reinforcement learning is the development of such algorithms which have been able to achieve the human level functioning, and the core concept behind this algorithm is the deep Q learning. The latter is an algorithm that produces a Q-table which the agent uses to find the right action that needs to be taken in a given condition or circumstances.

Reinforcement learning solves goal oriented problems by incorporating neural networks. With this combination, it can beat human experts playing different games like Atari video games.

Well, the objective of reinforcement learning and deep Q learning is to create a system that surpasses human activities and gives more flawless results without consuming much time.

These are a few of the remarkable developments in machine learning that have transformed how things operate. The technology is highly dynamic, and it will continue to grow in the times to come.

What's next?

If you too wish to be a part of this change, this is the time to enroll in the certification program in machine learning and deep learning. Global Tech Council, one of the leading platforms, offers the best online courses, wherein you will learn about the most advanced technologies like machine learning, deep learning, neural network, and reinforcement learning. The course is not limited to conceptual learning, but you will also gain insight into the practical applications. So begin your learning journey today, and enroll with the Global Tech Council.