<prompt> Theha neural network ho bolela esale pele hore na ke lifilimi life tse tla atleha.nan  <response>: Instruction in English: Build a neural network to predict which movies will be successful. . Response in English: To build a neural network to predict which movies will be successful, you will need to follow several steps:1. Gather and preprocess data: You first need to gather relevant data on movies, such as their budgets, genres, cast, reviews, ratings, and release dates. Once you have your data, you should preprocess it by normalizing or scaling any numerical data and encoding any categorical data, such as genres or cast members.2. Select a model architecture: Next, you should choose an appropriate architecture for your neural network. This could include a multi-layer perceptron or a convolutional neural network. The number and size of hidden layers, as well as the activation functions used, may need to be adjusted according to your data.3. Train the model: Once you have your preprocessed data and model architecture, you should split your data into training and validation sets and train your model using the training data. You should monitor the models performance on the validation data and adjust its hyperparameters, such as the learning rate, as needed to improve its performance.4. Evaluate the model: Once your model has been trained, you should evaluate its performance on a test dataset. This will give you an idea of how well the model is likely to perform in predicting the success of movies that it has not seen before.5. Make predictions: Finally, you can use your trained and validated model to make predictions about the success of new or upcoming movies. The model should output probabilities of success, which you can interpret to make decisions or recommendations about the movies in question.Remember, building a neural network is an iterative process, and you may need to try several different architectures, preprocessing techniques, and hyperparameters before arriving at a model that performs well on your data. . Response in Sesotho: Ho theha marang-rang a neural ho bolela esale pele hore na ke lifilimi life tse tla atleha, ho tla hlokahala hore u latele mehato e maloa: 1. Bokella me u lokise lintlha tsa pele : Pele u lokela ho bokella lintlha tse amanang le lifilimi, tse kang lichelete tsa tsona, mefuta ea tsona, mefuta ea tsona. , maikutlo, lintlha, le matsatsi a ho lokolloa. Ha u se u e-na le data ea hau, u lokela ho e lokisa esale pele ka ho tloaeleha kapa ka ho lekanya lintlha leha e le life tsa linomoro le ho kenya data leha e le efe ea lihlopha, tse kang mefuta kapa litho tsa sehlopha. 2. Kgetha moetso wa meralo: Ka mora moo, o lokela ho khetha sebopeho se nepahetseng bakeng sa netweke ea hau ea methapo. Sena se ka kenyelletsa multi-layer perceptron kapa convolutional neural network. Palo le boholo ba likarolo tse patiloeng, hammoho le mesebetsi ea tsebetso e sebelisitsoeng, e kanna ea hloka ho lokisoa ho latela data ea hau. 3. Koetlisa mohlala: Hang ha u se u e-na le data ea hau e entsoeng esale pele le meralo ea moralo, u lokela ho arola lintlha tsa hau ka lihlopha tsa koetliso le ho netefatsa me u koetlise mohlala oa hau u sebelisa lintlha tsa koetliso. U lokela ho beha leihlo tšebetso ea mohlala ho data ea netefatso le ho lokisa li-hyperparameter tsa eona, joalo ka sekhahla sa ho ithuta, kamoo ho hlokahalang ho ntlafatsa tsebetso ea eona. 4. Hlahloba mohlala: Hang ha moetso oa hau o se o koetlisoa, u lokela ho lekola tšebetso ea oona pokellong ea boitsebiso ea teko. Sena se tla u fa leseli la hore na mohlala ona o ka sebetsa hantle hakae ho bolela esale pele katleho ea lifilimi tseo o e-so ka o li bona pele. 5. Etsa likhakanyo : Qetellong, u ka sebelisa mohlala oa hau o koetlisitsoeng le o tiisitsoeng ho etsa likhakanyo mabapi le katleho ea lifilimi tse ncha kapa tse tlang. Mohlala o lokela ho hlahisa menyetla ea katleho, eo u ka e tolokang ho etsa liqeto kapa likhothaletso mabapi le lifilimi tseo ho buuoang ka tsona. Hopola, ho aha marang-rang a marang-rang ke tsebetso e pheta-phetoang, me u ka hloka ho leka meaho e mengata e fapaneng, mekhoa ea ho lokisa esale pele, le li-hyperparameter pele u fihla moetsong o sebetsang hantle ho data ea hau. 