Deep Learning and Neural Networks in Data Science

Neural networks are arguably the most sophisticated and cutting-edge models in machine learning. Inspired by the structure of the human brain, neural networks have established a reputation for successfully learning complex tasks such as object recognition in images, automatic speech recognition (ASR), machine translation, image captioning, video classification etc.

In the following video, Ujjyaini will give you a glimpse of deep learning (and neural networks).

As you increase the number of layers of neurons in a neural network, the whole architecture tends to get 'deeper'. It then is called deep learning algorithm.

One of the most important applications of neural networks is automatic game playing. To learn q game, an untrained neural network plays the game millions of times. At first, it plays completely randomly, but over time the system learns from wins, losses, and draws to adjust the parameters of the neural network, making it more likely to choose advantageous moves in the future. Google's  Deepmind uses the same technique.

In the next segment, you will see how text data is interpreted through natural language processing.

You can learn more about the applications of deep learning architecture here - Deep Learning and Neural Network