Machine learning (ML) is the study of computer algorithms that improve automatically through experience.[1][2] It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.[3] Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. ... See more
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.[1] It infers a function from labeled training data consisting of a set of training examples.[2] In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). ... See more
- 0x00-binary_classification
- 0x01-multiclass_classification
- 0x02-tensorflow
- 0x03-optimization
- 0x04-error_analysis
- 0x05-regularization
- 0x06-keras
- 0x07-cnn
- 0x08-deep_cnns
- 0x09-transfer_learning
- 0x0A-object_detection
- 0x0B-face_verification
- 0x0C-neural_style_transfer
- 0x0D-RNNs
- 0x0E-time_series
Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. In contrast to supervised learning that usually makes use of human-labeled data, unsupervised learning, also known as self-organization allows for modeling of probability densities over inputs. ... See more