What is the difference between deep learning and big data?

The concept of deep learning originated from the study of artificial neural networks. A multilayer perceptron with multiple hidden layers is an example of a deep learning structure. Deep learning discovers distributed feature representations of data by combining low-level features to form more abstract high-level representation attribute categories or features. [1]

The concept of deep learning was proposed by Hinton et al. in 2006. Based on Deep Belief Network (DBN), an unsupervised greedy layer-by-layer training algorithm was proposed, which brought hope for solving the optimization problems related to deep structures, and then a multi-layer autoencoder deep structure was proposed. In addition, the convolutional neural network proposed by Lecun et al. is the first true multilayer structure learning algorithm, which uses spatial relatives to reduce the number of parameters to improve training performance.

1] Deep learning is a method in machine learning based on representation learning of data. Observations, such as an image, can be represented in a variety of ways, such as a vector of intensity values per pixel, or more abstractly as a series of edges, a region of a specific shape, and so on. It is easier to learn tasks from instances using certain representations (e.g., face recognition or facial expression recognition).

The benefit of deep learning is that it replaces manual feature acquisition with unsupervised or semi-supervised feature learning and hierarchical feature extraction efficient algorithms.

Machine learning is a discipline that studies how computers simulate or implement human learning behaviors in order to acquire new knowledge or skills, and to reorganize existing knowledge structures to continuously improve their performance. Can machines learn like humans? In 1959, Samuel in the United States designed a chess program that had the ability to learn and improve his chess skills through constant play.

4 years later, this program prevailed over the designers themselves. After another 3 years, the program defeated an undefeated champion in the United States who had been winning for 8 years. This program shows people the power of machine learning and asks many thought-provoking social and philosophical questions.

Deep learning is a new research direction in the field of machine learning, which has been introduced into machine learning to bring it closer to the original goal - artificial intelligence.

Deep learning is the study of the intrinsic rules and representation levels of sample data, and the information obtained during these learning processes is of great help to the interpretation of data such as text, images, and sounds.

Deep learning is the study of the intrinsic rules and representation levels of sample data, and the information obtained during these learning processes is of great help to the interpretation of data such as text, images, and sounds. The ultimate goal is for machines to be able to learn analytically like humans, and to recognize data such as text, images, and sounds. Deep learning is a sophisticated machine learning algorithm that achieves far more performance in speech and image recognition than previous technologies.