Deep Learning

What is Deep Learning?

Deep is a technical term. To understand, deep learning, you first need to understand how neural network works. Here is a video explaining that.

Neural networks consist of a network of layers. A shallow network has one hidden layer while a deep network has more than one hidden layer. Multiple hidden layers allow deep neural networks to learn features of the data in a feature hierarchy, because simple features e.g. two pixels recombine from one layer to the next, form more complex features e.g. a line.

Networks with many layers pass input data (features) through more mathematical operations than networks with few layers, and are consequently more computationally intensive to train. Computational intensity is one of the hallmarks of deep learning, and it is one reason why a new kind of chip call GPUs is in demand to train deep-learning models. It takes many hours and sometimes days to train a deep neural network using the normal CPU computers.

Application of Deep Learning in real life

Deep Learning: Examples include: For Object Recognition in self driving cars so that cars can detect other motor vehicles, road edges, and pedestrians among others, security to detect violence in smart cities, detect fake money in automated payment systems such as car parks and Automated Teller Machines, Facial recognition, Speech recognition.

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