<prompt> Etsa lethathamo la mekhoa e mehlano e tebileng ea ho ithuta.nan  <response>: Instruction in English: Make a list of five deep learning techniques. . Response in English: Here is a list of five deep learning techniques: 1. Artificial Neural Networks ANN: A computing system that is inspired by the biological neural networks that make up animal brains. ANNs are composed of several layers of interconnected nodes, where each layer processes information and passes it on to the next.2. Convolutional Neural Networks CNN: A type of ANN that is designed for image recognition and classification. CNNs have multiple layers that are specialized to detect and recognize different features, such as edges or corners, in the input images.3. Recurrent Neural Networks RNN: A type of ANN designed to process sequential data, such as text or time series. RNNs have a looping mechanism that allows them to retain information from previous time steps, making them well-suited for tasks such as language translation and speech recognition.4. Deep Belief Networks DBN: A type of generative model composed of multiple layers of stacked Restricted Boltzmann Machines RBM, which are trained in an unsupervised manner. DBNs can be used for tasks such as feature extraction, representation learning, and dimensionality reduction.5. Generative Adversarial Networks GAN: A type of neural network architecture where two neural networks, called the generator and the discriminator, are trained simultaneously to learn a data distribution. The generator produces fake data samples, while the discriminator determines if the generated data is real or fake. GANs can be used for tasks such as data generation, image synthesis, and style transfer. . Response in Sesotho: Mona ke lethathamo la mekhoa e mehlano e tebileng ea ho ithuta: 1. Artificial Neural Networks ANN: Sistimi ea komporo e susumetsoang ke marang-rang a methapo ea kutlo a etsang boko ba liphoofolo. Li-ANN li entsoe ka likarolo tse maloa tsa li-node tse hokahaneng, moo karolo e ngoe le e ngoe e sebetsanang le tlhahisoleseding ebe e e fetisetsa ho e latelang. 2. Convolutional Neural Networks CNN: Mofuta oa ANN o etselitsoeng ho lemoha le ho hlophisa litšoantšo. Li-CNN li na le mekhahlelo e mengata e ikhethileng ho lemoha le ho lemoha likarolo tse fapaneng, joalo ka mathōko kapa likhutlo, litšoantšong tse kentsoeng. 3. Recurrent Neural Networks RNN: Mofuta oa ANN o etselitsoeng ho sebetsana le lintlha tse latellanang, tse kang mongolo kapa letoto la nako. Li-RNN li na le mochine o li lumellang ho boloka boitsebiso bo tsoang mehatong e fetileng, ho etsa hore e lokele mesebetsi e kang ho fetolela puo le temoho ea puo. 4. Deep Belief Networks DBN: Mofuta oa mofuta oa generative o entsoeng ka mekhahlelo e mengata ea Mechini ea Boltzmann e thibetsoeng e thibetsoeng RBM, e koetlisitsoeng ka mokhoa o sa laoleheng. Li-DBN li ka sebelisoa bakeng sa mesebetsi e kang ho ntša likarolo, ho ithuta ka boemeli, le ho fokotsa boholo. 5. Generative Adversarial Networks GAN: Mofuta oa meralo ea marang-rang a marang-rang moo marang-rang a mabeli a methapo, a bitsoang jenereithara le khethollo, a koetlisetsoang ka nako e le ngoe ho ithuta kabo ea data. Jenereithara e hlahisa lisampole tsa data tse seng tsa nnete, ha mokhethoa a etsa qeto ea hore na data e hlahisitsoeng ke ea nnete kapa ke leshano. Li-GAN li ka sebelisoa bakeng sa mesebetsi e kang ho hlahisa data, ho kopanya litšoantšo, le phetisetso ea setaele. 