It depends on how you define "main fields of artificial intelligence".

Deep Learning works great for several problems, which can be called "weak AI", such as Computer Vision, Speech Recognition, Machine Translation, etc.

(https://en.wikipedia.org/wiki/Weak_AI)

When it comes to the "strong AI" - I personally think that much more than just Deep Learning based system will be needed.

(https://en.wikipedia.org/wiki/Strong_AI)

Hope it helps.

It is a techique of neural networks with propouses of patter recognition.

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of neural network methods based on convolutional neural networks (CNN)s. Learning can be supervised, semi-supervised or unsupervised.

Deep learning architectures such as deep neural networks, deep belief networks recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

Neural networks were originally inspired by information processing and distributed communication nodes in biological nervous systems yet have various differences from the structural and functional properties of biological brains (especially human brains), which make them incompatible with neuroscience evidences. Specifically, Neural Networks tend to be static and symbolic, while the human brain is dynamic and analog.

Techopedia explains Deep Learning

Deep learning is a specific approach used for building and training neural networks, which are considered highly promising decision-making nodes. An algorithm is considered to be deep if the input data is passed through a series of nonlinearities or nonlinear transformations before it becomes output. In contrast, most modern machine learning algorithms are considered "shallow" because the input can only go only a few levels of subroutine calling.

Deep learning removes the manual identification of features in data and, instead, relies on whatever training process it has in order to discover the useful patterns in the input examples. This makes training the neural network easier and faster, and it can yield a better result that advances the field of artificial intelligence

See more :

https://machinelearningmastery.com/what-is-deep-learning/

Artificial intelligence can be classified into three different types of systems: analytical, human-inspired, and humanized artificial intelligence

Artificial intelligence can be classified into three different types of systems: analytical, human-inspired, and humanized artificial intelligence

There are many practical applications of Deep learning, such computer vision, speech recognition, image processing , classification, prediction and so.

Deep Learning is a way to do AI. A powerfull way with very assertive techniques in many applications.