human-annotated labels--you can think of it as supervised learning without any humans in the loop there are still labels involved (because the learning has to be supervised by something)but they're generated from the input datatypically using heuristic algorithm for instanceautoencoders are well-known instance of self-supervised learningwhere the generated targets are the inputunmodified in the same waytrying to predict the next frame in videogiven past framesor the next word in textgiven previous wordsare instances of self-supervised learning (temporally supervised learningin this casesupervision comes from future input datanote that the distinction between supervisedself-supervisedand unsupervised learning can be blurry sometimes--these categories are more of continuum without solid borders self-supervised learning can be reinterpreted as either supervised or unsupervised learningdepending on whether you pay attention to the learning mechanism or to the context of its application in this bookwe'll focus specifically on supervised learningbecause it' by far the dominant form of deep learning todaywith wide range of industry applications we'll also take briefer look at self-supervised learning in later note reinforcement learning long overlookedthis branch of machine learning recently started to get lot of attention after google deepmind successfully applied it to learning to play atari games (andlaterlearning to play go at the highest levelin reinforcement learningan agent receives information about its environment and learns to choose actions that will maximize some reward for instancea neural network that "looksat videogame screen and outputs game actions in order to maximize its score can be trained via reinforcement learning currentlyreinforcement learning is mostly research area and hasn' yet had significant practical successes beyond games in timehoweverwe expect to see reinforcement learning take over an increasingly large range of real-world applicationsself-driving carsroboticsresource managementeducationand so on it' an idea whose time has comeor will come soon classification and regression glossary classification and regression involve many specialized terms you've come across some of them in earlier examplesand you'll see more of them in future they have precisemachine-learning-specific definitionsand you should be familiar with themsample or input--one data point that goes into your model prediction or output--what comes out of your model target--the truth what your model should ideally have predictedaccording to an external source of data licensed to