<prompt> Theha marang-rang a marang-rang bakeng sa ho bolela esale pele litheko tsa setoko marakeng o fanoeng.Shanghai Stock Exchange  <response>: Instruction in English: Build a neural network for predicting stock prices in the given market. . Response in English: Sure, to build a neural network for predicting stock prices in the Shanghai Stock Exchange, we would need to follow these steps:1. Collect and preprocess data: We would first need to collect historical data on stock prices in the Shanghai Stock Exchange. This would involve gathering data on stock prices, volume, and other relevant financial indicators. Once we have the data, we will need to preprocess it by cleaning, normalizing, and transforming it into a format suitable for training our neural network. 2. Select the model architecture: Next, we need to select the architecture for our neural network. This would involve choosing the number of layers, neurons, and activation functions for our model. For stock price prediction, a recurrent neural network RNN such as LSTM Long Short-Term Memory or GRU Gated Recurrent Unit might be a good choice, as they are well-suited to handling sequential data such as time series data.3. Train the model: Once we have selected our model architecture, we need to train our neural network. This would involve splitting our data into training and testing sets, and then using the training data to update the weights and biases of our neural network. We would need to select an appropriate loss function, such as mean squared error, to measure how well our model is performing, and an optimization algorithm, such as gradient descent, to update the weights and biases of our model.4. Evaluate the model: After training our model, we need to evaluate how well it is performing. This could involve using our testing data to compute metrics such as mean squared error, mean absolute error, or R-squared value. We can also visualize the models predictions against the actual data to see how well it is performing.5. Make predictions: Once we are satisfied with our models performance, we can use it to make predictions on new data. For example, we can provide our model with data on recent stock prices and other financial indicators, and it can generate a prediction for the future stock price.In summary, building a neural network for predicting stock prices in the Shanghai Stock Exchange would involve collecting and preprocessing data, selecting the model architecture, training the model, evaluating its performance, and then using it to make predictions on new data. . Response in Sesotho: Ehlile, ho aha marang-rang a marang-rang bakeng sa ho bolela esale pele litheko tsa setoko ho Shanghai Stock Exchange, re tla hloka ho latela mehato ena: 1. Bokella le ho lokisa data esale pele : Re ne re tla hloka pele ho bokella lintlha tsa nalane ka litheko tsa setoko Shanghai. Stock Exchange. Sena se tla kenyelletsa ho bokella lintlha ka litheko tsa setoko, bophahamo ba modumo, le matšoao a mang a bohlokoa a lichelete. Ha re se re na le data, re tla hloka ho e lokisa esale pele ka ho e hloekisa, ho e tloaeleha, le ho e fetola hore e be sebopeho se loketseng ho koetlisa marang-rang a rona a neural. 2. Khetha sebopeho sa mohlala : Ka mora moo, re hloka ho khetha sebopeho sa marang-rang a rona a methapo. Sena se ne se tla kenyelletsa ho khetha palo ea likarolo, li-neurone, le tsebetso ea tsebetso bakeng sa mohlala oa rona. Bakeng sa ponelopele ea theko ea setoko, marang-rang a tloaelehileng a neural RNN joalo ka LSTM Long Short-Term Memory kapa GRU Gated Recurrent Unit e kanna ea ba khetho e ntle, kaha e loketse ho sebetsana le data e latellanang joalo ka data ea letoto la nako. . 3. Koetlisa mohlala: Ha re se re khethile moetso oa rona oa meralo, re hloka ho koetlisa marang-rang a rona a methapo. Sena se tla kenyelletsa ho arola lintlha tsa rona ho li-sete tsa koetliso le liteko, ebe re sebelisa lintlha tsa koetliso ho ntlafatsa boima le leeme la netweke ea rona ea methapo. Re tla hloka ho khetha tsebetso e nepahetseng ea tahlehelo, joalo ka phoso ea squared error, ho lekanya hore na mohlala oa rona o sebetsa hantle hakae, le algorithm ea ho ntlafatsa, joalo ka gradient descent, ho ntlafatsa boima le litšekamelo tsa mohlala oa rona. 4. Hlahloba mohlala: Kamora ho koetlisa mohlala oa rona, re hloka ho lekola hore na o sebetsa hantle hakae. Sena se ka kenyelletsa ho sebelisa lintlha tsa rona tsa tlhahlobo ho kopanya metrics joalo ka phoso ea moelelo oa lisekoere, phoso e hlakileng, kapa boleng ba R-squared. Hape re ka bona ka mahlo a kelello likhakanyo tsa mohlala khahlano le data ea nete ho bona hore na e sebetsa hantle hakae. 5. Etsa likhakanyo: Hang ha re khotsofetse ke tšebetso ea mohlala oa rona, re ka e sebelisa ho etsa likhakanyo tsa data e ncha. Ka mohlala, re ka fana ka mohlala oa rona ka lintlha tsa litheko tsa morao-rao le matšoao a mang a lichelete, me e ka hlahisa esale pele bakeng sa theko ea theko e tlang. Ka kakaretso, ho haha marang-rang a marang-rang bakeng sa ho bolela esale pele litheko tsa setoko ho Shanghai Stock Exchange ho ne ho tla kenyelletsa ho bokella le ho lokisa lintlha, ho khetha moralo oa mohlala, ho koetlisa mohlala, ho lekola tsebetso ea ona, ebe o e sebelisa ho etsa likhakanyo ho data e ncha. 