figure - clearly depicts how the unrolled network will accept sequences of length in each pass of the input data and operate on the same long short-term memory networks rnns are good in working on sequence based data but as the sequences start increasingthey start losing historical context over time in the sequence and hence outputs are not always what is desired this is where long short-term memory networkspopularly known as lstmscome into the pictureintroduced by hochreiter schmidhuber in lstms can remember information from really long sequence based data and prevent issues like the vanishing gradient problemwhich typically occurs in anns trained with backpropagation lstms usually consist of three or four gatesincluding inputoutputand special forget gate figure - shows high-level pictorial representation of single lstm cell figure - an lstm cell (sourcedeeplearning netthe input gate usually can allow or deny incoming signals or inputs to alter the memory cell state the output gate usually propagates the value to other neurons as needed the forget gate controls the memory cell' self-recurrent connection to remember or forget previous states as necessary multiple lstm cells are usually stacked in any deep learning network to solve real-world problems like sequence prediction autoencoders an autoencoder is specialized artificial neural network that is primarily used for performing unsupervised machine learning tasks its main objective is to learn data representationsapproximationsand encodings autoencoders can be used for building generative modelsperforming dimensionality reductionand detecting anomalies machine learning methods machine learning has multiple algorithmstechniquesand methodologies that can be used to build models to solve real-world problems using data this section tries to classify these machine learning methods under some broad categories to give some sense to the overall landscape of machine learning methods that are ultimately used to perform specific machine learning tasks we discussed in previous section typically the same machine learning methods can be classified in multiple ways under multiple umbrellas following are some of the major broad areas of machine learning methods