<prompt> Hlahisa meralo ea marang-rang ea neural bakeng sa mochine o bonahalang oa binary.nan  <response>: Instruction in English: Generate a neural network architecture for a binary visible object detector. . Response in English: A binary visible object detector is a model that helps to detect the presence or absence of an object in an image. The following is a possible Neural Network architecture for a binary visible object detector: Input Layer: The input layer takes the image pixels as input. The image is usually resized and processed to have a fixed size and normalized pixel values before being fed into the input layer.Convolutional Layer: The first hidden layer is a convolutional layer that applies multiple filters to the input image to extract features. The convolutional layer is usually followed by a ReLU Rectified Linear Unit activation function, which introduces non-linearity into the model.Pooling Layer: The next layer is a pooling layer, which down-samples the extracted features to reduce the dimensionality, and helps in achieving translation invariance.At this point, the architecture can have additional convolutional and pooling layers, usually with an increasing number of filters in each successive convolutional layer.Flattening Layer: After the last convolutional and pooling layer, a flattening layer is applied to convert the multi-dimensional feature maps into a 1D feature vector.Fully Connected Layer: Next, there is a fully-connected layer that takes the flattened 1D vector as input and calculates the weighted sum of all the inputs. This is followed by another ReLU activation function to add non-linearity.Output Layer: Finally, there is an output layer with a single neuron that calculates the probability of the object being present in the image. The output layer uses a sigmoid activation function to ensure that the output value is between 0 and 1, representing the probability of the object being present in the image.This architecture can be trained using a binary cross-entropy loss function, which measures the difference between the predicted probability and the actual label 0 or 1 of the object being present in the image.Overall, the binary visible object detector neural network architecture consists of multiple layers including input, convolutional, pooling, flattening, fully-connected, and output layers to detect the presence or absence of an object in the image. . Response in Sesotho: Mohloli oa ntho e bonahalang ea binary ke mohlala o thusang ho lemoha boteng kapa ho se be teng ha ntho e setšoantšong. Se latelang ke mokhoa oa ho aha oa Neural Network bakeng sa mochine o bonahalang oa binary: Layer Input: Lera le kenang le nka lipikselse tsa setšoantšo e le kenyeletso. Setšoantšo hangata se fetoloa boholo le ho sebetsoa hore se be le boholo bo tsitsitseng le boleng bo tloaelehileng ba pixel pele se kengoa ka hara sekhahla sa ho kenya. Convolutional Layer: Lera la pele le patiloeng ke lera la convolutional le sebelisang li-filters tse ngata setšoantšong se kentsoeng ho ntša likarolo. Lera la convolutional hangata le lateloa ke tsebetso ea tsebetso ea ReLU Rectified Linear Unit, e hlahisang ho se lumellane ho latela mohlala. Pooling Layer: Lera le latelang ke lesela le kopantseng, le theolang lisampole tsa likarolo tse nkiloeng ho fokotsa boholo, le ho thusa ho fihlela phetoho ea phetolelo. Mothating ona, meaho e ka ba le likarolo tse ling tsa convolutional le tse kopanyang, hangata ka palo e ntseng e eketseha ea li-filters karolong e ngoe le e ngoe e latellanang ea convolutional. Layer e bataletseng: Kamora lera la ho qetela la convolutional and pooling, ho sebelisoa lesela le bataletseng ho fetolela limmapa tsa likarolo tse ngata hore e be vector ea 1D. Lera le Hoketsoeng ka Botlalo: Ka mora moo, ho na le lera le hoketsoeng ka botlalo le nkang vector e bataletseng ea 1D e le kenyelletso le ho bala kakaretso e lekantsoeng ea lintho tsohle tse kentsoeng. Sena se lateloa ke tsebetso e ngoe ea tsebetso ea ReLU ho eketsa non-linearity. Lera la Output: Qetellong, ho na le lera la tlhahiso le nang le neuron e le ngoe e lekanyang monyetla oa hore ntho e be teng setšoantšong. Sekhahla sa tlhahiso se sebelisa tsebetso ea sigmoid ho netefatsa hore boleng ba tlhahiso bo pakeng tsa 0 le 1, e emelang monyetla oa hore ntho e be teng setšoantšong. Moaho ona o ka koetlisoa ho sebelisoa tsebetso ea tahlehelo ea binary cross-entropy, e lekanyang phapang lipakeng tsa monyetla o boletsoeng esale pele le leibole ea nete 0 kapa 1 ea ntho e teng setšoantšong. Ka kakaretso, meralo ea binary ebonakalayo detector neural network architecture e na le mekhahlelo e mengata e kenyelletsang ho kenya letsoho, convolutional, pooling, flattening, e hokahaneng ka botlalo, le likarolo tse hlahisoang ho bona boteng kapa ho se be teng ha ntho setšoantšong. 