Introduction to Deep Learning

Deep Learning is a type of machine learning in which a model learns to perform classification tasks directly from images, text or sound. Its usually implemented using a neural network architecture. Here deep refers to the "Number of layers in the network" i.e the more layers, the deeper the network. Traditional neural network contain only 2-3 layers, but in deep network contains hundreds of layers.

Deep learning example is A self-driving vehicle slows down as it approaches a pedestrian network.

Deep learning is especially well-suited to identification applications such as face recognition, text translation, voice recognition and advanced driver assistance systems and traffic sign recognition etc. Deep learning is growing faster because it really works. 

What is the Difference Between Deep Learning and Machine Learning?

Deep learning is a sub-type of machine learning. For example with machine learning, you manually extract the relevant features of an image. With deep learning, you feed the raw images directly into a deep neural network that learns the features automatically. Deep learning often requires hundreds of thousands or millions of images for the best results. It’s also computationally intensive and requires a high-performance GPU.