Artificial neural networks are a crude abstraction of real neurons. There are various reasons that make AI researchers look at the human brain as an inspiration to develop such networks.
From machine translation that connects humans across cultures, to conversational chatbots that help with customer service; from sentiment analysis that deeply understands a human’s mood, to attention mechanisms that can mimic our visual attention, the field of NLP is too expansive to cover completely, so I’d encourage you to explore it further, whether through online courses, blog tutorials, or research papers.
NLP is certainly one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Fully understanding and representing the meaning of language is an extremely difficult goal. Why? Because the human language is quite special.
Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science).
Today, deep neural networks and deep learning achieve outstanding performance on many important problems in computer vision, speech recognition, and natural language processing. They’re being deployed on a large scale by companies such as Google, Microsoft, and Facebook.I hope that this post helps you learn the core concepts of neural networks, including modern techniques for deep learning.