Deep Learning Demystified: A Beginner-Friendly Introduction

Deep learning is a subdomain of machine learning that is based on the use of artificial neural networks. It is called “deep” learning because the neural networks used in deep learning are typically composed of many layers, which enables them to learn complex patterns and relationships in the data.

Deep learning is commonly used for various tasks such as image, speech recognition, and natural language processing, and predictive modeling. It has achieved state-of-the-art results in a number of different applications, and it has the potential to be able to transform a wide range of industries and fields.

Deep learning is based on the idea of building artificial neural networks that are composed of layers of interconnected nodes, or “neurons.” These networks are trained using large datasets, and they are able to learn patterns and relationships in the data by adjusting the connections between the neurons. Once trained, the network can be used to make predictions or take decisions based on new, unseen data.

Overall, deep learning is a powerful and flexible tool that is widely used in a variety of applications, including image as well as speech recognition, natural language processing, and predictive modeling. It has the potential to transform a wide range of industries and fields, and it is an exciting and rapidly evolving area of research and development.

Machine learning can be seen as a  subset of deep learning. It is a field that relies on studying computer algorithms to learn and advance on its own. Deep learning uses artificial neural networks, which are created to mimic how humans think and learn, whereas machine learning uses simpler principles. Up until recently, the complexity of neural networks was constrained by computational capacity.