<prompt> Hlalosa likarolo tsa mantlha tsa perceptron.nan  <response>: Instruction in English: Outline the main features of a perceptron. . Response in English: A Perceptron is a type of artificial neural network that is widely used for binary classification and prediction tasks. Here are some of its main features:1. Single-layer architecture: The perceptron consists of a single layer of weights that are adjusted during training to find the best decision boundary.2. Linear separability: Perceptrons can only accurately classify data that is linearly separable.3. Binary classification: Perceptrons are used for binary classification tasks, where the output is either 0 or 1.4. Activation function: The Perceptron uses the step function or sign function as its activation function. This determines whether the neuron is activated outputs 1 or not outputs 0.5. Learning algorithm: The Perceptron uses a supervised learning algorithm, where the model is trained by adjusting the weights based on the differences between the predicted output and the desired output.6. Online learning: Perceptron can also learn incrementally when presented with new observations one at a time, this is called online learning.7. Preprocessing: Preprocessing of input data such as normalization or scaling may be necessary for a perceptron to reach optimal results.In summary, a Perceptron is a simple, yet powerful, tool for binary classification tasks that are linearly separable. Its simple architecture and learning algorithm can enable fast training and real-time predictions. . Response in Sesotho: Perceptron ke mofuta oa marang-rang a maiketsetso a maiketsetso a sebelisoang haholo bakeng sa likarolo tsa binary le mesebetsi ea ho bolela esale pele. Mona ke tse ling tsa likarolo tsa eona tse ka sehloohong: 1. Mehaho ea lera le le leng: Perceptron e na le lera le le leng la boima bo lokisoang nakong ea koetliso ho fumana moeli o motle oa liqeto. 2. Karohano ea mela: Li-Perceptron li ka arola ka nepo feela data e ka arohanngoang ka tatellano. 3. Karolelano ea binary: Li-perceptron li sebelisoa bakeng sa mesebetsi ea lihlopha tsa binary, moo tlhahiso e ka bang 0 kapa 1. 4. Tsebetso ea tsebetso: Perceptron e sebelisa mohato oa mohato kapa tsebetso ea pontšo e le tsebetso ea eona ea tsebetso. Sena se etsa qeto ea hore na neuron e kentsoe tšebetsong lihlahisoa 1 kapa che liphetho 0. 5. Algorithm ea ho ithuta: Perceptron e sebelisa algorithm ea ho ithuta e hlokometsoeng, moo mohlala o koetlisoang ka ho lokisa litekanyo ho latela phapang pakeng tsa tlhahiso e boletsoeng esale pele le tlhahiso e lakatsehang. 6. Ho ithuta inthaneteng: Perceptron e ka boela ea ithuta ka ho eketseha ha e hlahisoa ka litebello tse ncha ka nako, sena se bitsoa ho ithuta inthaneteng. 7. Ho sebetsa esale pele: Ho lokisa esale pele data e kentsoeng joalo ka ho tloaeleha kapa ho lekanya ho ka hlokahala hore perceptron e fihlele liphetho tse nepahetseng. Ka bokhutsoane, Perceptron ke sesebelisoa se bonolo, empa se matla, bakeng sa mesebetsi ea lihlopha tsa binary tse arohanngoang ka tatellano. Meaho ea eona e bonolo le algorithm ea ho ithuta e ka thusa koetliso e potlakileng le likhakanyo tsa nako ea nnete. 