Deep learning is a subset of machine learning that is concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Neural networks are trained using a large set of data, known as a training set, in order to learn to recognize patterns. After training, the neural network is able to make predictions about new data, known as the test set.
The term “deep” in deep learning refers to the number of hidden layers in the neural network. Deep learning neural networks have more than one hidden layer, and the extra layers allow the network to learn more complex patterns.
The current state-of-the-art in deep learning is the use of large language models (LLMs). LLMs are neural networks that have been pre-trained on a large amount of text data. The pre-training allowed the neural network to learn the general structure of language, and the LLM can then be fine-tuned on a specific task, such as sentiment analysis or question answering.
There are many potential applications for deep learning, including image classification, object detection, and motion estimation. However, the most promising application of deep learning is in the area of natural language processing (NLP). NLP is a subfield of AI that deals with the understanding and manipulation of human language.
Deep learning has achieved impressive results in a variety of NLP tasks, such as machine translation, text classification, and question answering. Deep learning is well suited to these tasks because they require the ability to learn from large amounts of data and to generalize from that data to new situations.
One of the challenges in deep learning is the amount of data required to train a neural network. Another challenge is the difficulty in understanding what the neural network has learned after training. As deep learning neural networks become more widely used, it will be important to develop methods for insights into the neural network’s “black box”.
References:
https://en.wikipedia.org/wiki/Deep_learning
https://en.wikipedia.org/wiki/Machine_learning
https://en.wikipedia.org/wiki/Artificial_neural_network
https://en.wikipedia.org/wiki/Training_set
https://en.wikipedia.org/wiki/Test_set
https://en.wikipedia.org/wiki/Natural_language_processing
https://en.wikipedia.org/wiki/Machine_translation
https://en.wikipedia.org/wiki/Text_classification