<prompt> Lera le kenyellelitsoeng le etsa eng ho neural network?nan  <response>: Instruction in English: What does an embedding layer do in a neural network? . Response in English: An embedding layer in a neural network is useful for transforming categorical data into a low-dimensional, dense and continuous numerical representation, often referred to as embedding vectors. This is particularly useful for handling text data, where words are mapped to numeric vectors.In natural language processing, a word embedding layer serves as a lookup table that maps numeric indices, which represent specific words, to their corresponding dense vectors. These vectors are learned based on the context in which words are used, so that words with similar meanings are placed closer together in the vector space. This makes the word representation more expressive, and can improve the performance of downstream models.An embedding layer can also be used to represent categorical variables in other types of data, such as users, products, or locations. This can be particularly helpful when there are large numbers of distinct categories, as the embedded representations are typically much lower-dimensional than other approaches to representing categorical variables, like one-hot encoding.Overall, an embedding layer can help a neural network to better understand and work with categorical data, improving its ability to identify patterns and relationships within the data. . Response in Sesotho: Lera le kenyellelitsoeng marang-rang a marang-rang a bohlokoa bakeng sa ho fetola data ea likarolo hore e be setšoantšo sa lipalo tse tlase, tse teteaneng le tse tsoelang pele, tse atisang ho bitsoa li-vectors tse kenang. Sena se bohlokoa haholo bakeng sa ho sebetsana le data ea mongolo, moo mantsoe a hlahisoang ho li-vector tsa linomoro. Tsebetsong ea puo ea tlhaho, lera le kenyang mantsoe le sebetsa e le tafole ea ho sheba e bontsang li-indices tsa linomoro, tse emelang mantsoe a itseng, ho li-vector tsa tsona tse teteaneng. Li-vector tsena li ithutoa ho ipapisitsoe le maemo ao mantsoe a sebelisoang ho ona, e le hore mantsoe a nang le meelelo e tšoanang a behoe haufinyana sebakeng sa vector. Sena se etsa hore kemiso ea mantsoe e be e hlakileng haholoanyane, me e ka ntlafatsa tsebetso ea mefuta e tlase. Lera le kentsoeng le ka boela la sebelisoa ho emela mefuta e fapaneng ea mefuta mefuteng e meng ea data, joalo ka basebelisi, lihlahisoa kapa libaka. Sena se ka thusa haholo ha ho na le likarolo tse ngata tse ikhethileng, kaha litšoantšo tse kentsoeng hangata li tlase haholo ho feta mekhoa e meng ea ho emela mefuta e fapaneng, joalo ka khouto e le ngoe. Ka kakaretso, lera le kenang le ka thusa marang-rang a neural ho utloisisa hamolemo le ho sebetsa ka lintlha tsa likarolo, ho ntlafatsa bokhoni ba eona ba ho tseba mekhoa le likamano ka hara data. 