<prompt> Ke melemo efe kapa mathata afe a marang-rang a maiketsetso a neural mabapi le scalability?nan  <response>: Instruction in English: What advantages or disadvantages of an artificial neural network in terms of scalability? . Response in English: An artificial neural network ANN is a computational model designed to simulate the structure and function of the human brain. It is capable of processing and solving complex problems through a network of interconnected nodes, called neurons.Scalability is an important characteristic to consider when evaluating the benefits of an artificial neural network. Below are some of the advantages and disadvantages of artificial neural networks in terms of scalability:Advantages:1. Adaptable to large datasets: ANNs are highly scalable when it comes to training on large datasets. With the right architecture, they can easily handle large volumes of data, making them ideal for use cases such as image recognition, speech recognition and natural language processing.2. Parallel processing: ANNs have the ability to perform parallel processing, meaning that multiple units can operate simultaneously. This allows for fast and efficient computation, especially when dealing with large datasets.3. Flexible architecture: The architecture of an ANN can be modified and adapted according to the size and complexity of the problem at hand. This makes ANNs highly scalable and able to solve a wide range of problems.Disadvantages:1. Computational complexity: Scalability with ANNs comes at a cost. The larger the network, the more computationally expensive it becomes to train it. This can make it challenging to scale ANNs for very large datasets, especially when computational resources are limited.2. Overfitting: As ANNs grow larger and more complex, they become more prone to overfitting. This means that they may perform well on the training data, but struggle to generalize when presented with new data. Overfitting can be mitigated by techniques such as early stopping, regularization and cross-validation, but it remains a challenge when scaling ANNs.3. Increasing training time: Training time increases as the size of the network and the dataset grows, making it more time-consuming to scale ANNs. This can be mitigated by techniques such as parallel computing, but it remains a challenge to efficiently train very large and complex networks. . Response in Sesotho: An artificial neural network ANN ke mokhoa oa computational o etselitsoeng ho etsisa sebopeho le tšebetso ea boko ba motho. E khona ho sebetsana le ho rarolla mathata a rarahaneng ka marang-rang a li-node tse hokahaneng, tse bitsoang li-neurons. Scalability ke tšobotsi ea bohlokoa e lokelang ho nahanoa ha ho hlahlojoa melemo ea marang-rang a maiketsetso. Ka tlase ke tse ling tsa melemo le melemo ea marang-rang a maiketsetso a marang-rang mabapi le scalability: Melemo: 1. Ho ikamahanya le li-dataset tse kholo: Li-ANN li senyeha haholo ha ho tluoa tabeng ea ho koetlisa li-dataset tse kholo. Ka boqapi bo nepahetseng, ba khona ho sebetsana le lintlha tse ngata habonolo, ho etsa hore e be tse loketseng maemo a tsebeliso joalo ka temoho ea litšoantšo, temoho ea puo le tsebetso ea puo ea tlhaho. 2. Tsebetso e tsoanang: Li-ANN li na le bokhoni ba ho sebetsa ka mokhoa o tsoanang, ho bolelang hore lihlopha tse ngata li ka sebetsa ka nako e le ngoe. Sena se lumella khomphutha e potlakileng le e sebetsang hantle, haholo ha e sebetsana le li-dataset tse kholo. 3. Mehaho e feto-fetohang: Mohaho oa ANN o ka fetoloa le ho fetoloa ho latela boholo le ho rarahana ha bothata bo teng. Sena se etsa hore li-ANN li senyehe haholo me li khone ho rarolla mathata a mangata. Go tlhoka mesola: 1. Ho rarahana ha computational: Scalability le ANNs e tla ka litšenyehelo. Ha marang-rang a le kholo, ho ba le chelete e ngata ea computationally ho e koetlisa. Sena se ka etsa hore ho be thata ho lekanya li-ANN bakeng sa li-dataset tse kholo haholo, haholo ha lisebelisoa tsa computational li lekanyelitsoe. 2. Ho tlatsa ho feta tekano: Ha li-ANN li ntse li hola le ho rarahana le ho feta, li ba le tšekamelo ea ho tlala ho feta tekano. Sena se bolela hore ba ka sebetsa hantle ho data ea koetliso, empa ba thatafalloa ke ho akaretsa ha ba hlahisoa ka lintlha tse ncha. Ho fetisoa ho feta tekano ho ka fokotsoa ka mekhoa e kang ho emisa kapele, ho etsa maemo le ho netefatsa, empa e ntse e le phephetso ha u phahamisa li-ANN. 3. Ho eketsa nako ea koetliso: Nako ea koetliso e ntse e eketseha ha boholo ba marang-rang le dataset e ntse e hōla, e leng se etsang hore ho be le nako e ngata ho lekanya li-ANN. Sena se ka fokotsoa ka mekhoa e kang parallel computing, empa e ntse e le phephetso ho koetlisa marang-rang a maholo haholo le a rarahaneng. 