This is not to say that building deep learning systems is relatively easy compared to conventional machine learning systems. With deep learning, all that is needed is to supply the system with a very large number of cat images, and the system can autonomously learn the features that represent a cat.įor many tasks, such as computer vision, speech recognition (also known as natural language processing), machine translation, and robotics, the performance of deep learning systems far exceeds that of conventional machine learning systems. In this example, a domain expert would need to spend considerable time engineering a conventional machine learning system to detect the features that represent a cat. It can classify groups of pixels that are representative of a cat’s features, with groups of features such as claws, ears, and eyes indicating the presence of a cat in an image.ĭeep learning is fundamentally different from conventional machine learning. This type of neural network typically learns from the pixels contained in the images it acquires. By building computational models that are composed of multiple processing layers, the networks can create multiple levels of abstraction to represent the data.įor example, a deep learning model known as a convolutional neural network can be trained using large numbers (as in millions) of images, such as those containing cats. How does deep learning work?ĭeep learning networks learn by discovering intricate structures in the data they experience. After machines have gained enough experience through deep learning, they can be put to work for specific tasks such as driving a car, detecting weeds in a field of crops, detecting diseases, inspecting machinery to identify faults, and so on. Unlike traditional machine learning algorithms, many of which have a finite capacity to learn no matter how much data they acquire, deep learning systems can improve their performance with access to more data: the machine version of more experience. The experiences through which machines can learn are defined by the data they acquire, and the quantity and quality of data determine how much they can learn.ĭeep learning is a branch of machine learning. In the artificial intelligence (AI) discipline known as deep learning, the same can be said for machines powered by AI hardware and software. The richer our experiences, the more we can learn.

Deep learning