underlying structure of the true data distribution even when there are no labels gans learn the underlying structure in the data through the training process and efficiently capture the structure using smallmanageable number of parameters this process is similar to the representation learning that occurs in deep learning each hidden layer in the neutral network of generator captures representation of the underlying data--starting very simply--and subsequent layers pick up more complicated representations by building on the simpler preceding layers using all these layers togetherthe generator learns the underlying structure of the data andusing what it has learnedthe generator attempts to create synthetic data that is nearly identical to the true data distribution if the generator has captured the essence of the true data distributionthe synthetic data will appear real sequential data problems using unsupervised learning unsupervised learning can also handle sequential data such as time series data one such approach involves learning the hidden states of markov model in the simple markov modelstates are fully observed and change stochastically (in other wordsrandomlyfuture states depend only on the current state and are not dependent on previous states in hidden markov modelthe states are only partially observablebutlike with simple markov modelsthe outputs of these partially observable states are fully observable since the observations that we have are insufficient to determine the state completelywe need unsupervised learning to help discover these hidden states more fully hidden markov model algorithms involve learning the probable next state given what we know about the sequence of previously occurringpartially observable states and fully observable outputs these algorithms have had major commercial applications in sequential data problems involving speechtextand time series reinforcement learning using unsupervised learning reinforcement learning is the third major branch of machine learningin which an agent determines its optimal behavior (actionsin an environment based on feedback (rewardthat it receives this feedback is known as the reinforcement signal the agent' goal is to maximize its cumulative reward over time while reinforcement learning has been around since the sit has made mainstream headline news only in recent years in deepmind--now owned by google--applied reinforcement learning to achieve superhuman-level performance at reinforcement learning using unsupervised learning