Deep learning

ML is about forecasting and solving complex problems using NLP, enabling computers to understand, interpret, and generate human language in a valuable and meaningful way. NLP is used in numerous applications, including language translation, sentiment analysis, chatbots, and voice assistants, allowing for more intuitive, human-like interaction with machines. While ML needs a pre-defined set of labeled data for supervised learning, deep learning uses a neural network for unsupervised learning to simulate human brain behaviors, using a large amount of data to develop ML capabilities. A neural network is a series of algorithms that recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

Deep learning involves a neural network of multiple layers where you don’t need to do data labeling upfront. However, depending on your use case, you can use both labeled and unlabeled data with deep learning. The following...