<prompt> Rala marang-rang a 3-layer neural bakeng sa ho rarolla bothata bo fanoeng.Ho khetholla lintja le likatse  <response>: Instruction in English: Design a 3-layer neural network for solving the given problem. . Response in English: Here is a possible design for a 3-layer neural network for classifying dogs and cats:Layer 1: Input Layer- This layer will receive the input data, which will be the features of the images of dogs and cats. Lets assume that the images are preprocessed and resized to a specific size, say 64x64 pixels, and are in grayscale. Thus, each image would have 64x644096 input pixels, which would translate to 4096 input neurons in this layer.Layer 2: Hidden Layer- This is a fully connected layer that will serve as the intermediary between the input layer and output layer. Here, we can choose a number of neurons based on our desired level of complexity for the model. Lets say we choose 128 neurons for this hidden layer, with a Rectified Linear Unit ReLU as the activation function. Layer 3: Output Layer- This is the final layer of the neural network that makes the final predictions on whether the input image is of a dog or a cat. Since this is a binary classification problem, we only need one neuron in this layer, with the Sigmoid function as the activation function. The Sigmoid function will output a probability score between 0 and 1, representing the likelihood of the input image being either a dog or a cat. A threshold, such as 0.5, is set to make the final classification: if the probability score is greater than or equal to the threshold, the image is classified as a dog, otherwise it is classified as a cat.Overall, this 3-layer neural network takes in preprocessed images of dogs and cats, processes the information in the hidden layer with the help of the activation function, and outputs a score representing the likelihood of the image belonging to one of the two classes. From this score, we can make our final classification using a threshold. . Response in Sesotho: Mona ke moralo o ka bang teng bakeng sa marang-rang a 3-layer neural bakeng sa ho khetholla lintja le likatse: Leaya 1: Lera la Input - Lera lena le tla fumana lintlha tse kentsoeng, e tla ba likarolo tsa litšoantšo tsa lintja le likatse. Ha re nke hore litšoantšo li hlophisitsoe esale pele me li fetotsoe boholo ba boholo bo itseng, re re lipikselse tse 64x64, me li ka greyscale. Ka hona, setšoantšo se seng le se seng se tla ba le 64x64 4096 lipikselse tsa ho kenya, tse ka fetolelang li-neuron tse 4096 sebakeng sena. Learo la 2: Lera le Patiloeng - Ena ke lera le hokahaneng ka botlalo le tla sebetsa e le mokena-lipakeng lipakeng tsa lera le kenang. Mona, re ka khetha li-neurone tse maloa ho latela boemo boo re bo batlang ba ho rarahana bakeng sa mohlala. Ha re re re khetha li-neurons tse 128 bakeng sa lera lena le patiloeng, le Rectified Linear Unit ReLU joalo ka tsebetso ea tsebetso. Lera 3: Lera la Output - Ena ke lera la ho qetela la netweke ea methapo e etsang likhakanyo tsa ho qetela tsa hore na setšoantšo se kentsoeng ke sa ntja kapa katse. Kaha sena ke bothata ba lihlopha tsa binary, re hloka neuron e le ngoe feela karolong ena, ka mosebetsi oa Sigmoid e le tsebetso ea tsebetso. Mosebetsi oa Sigmoid o tla hlahisa lintlha tsa monyetla lipakeng tsa 0 le 1, tse emelang monyetla oa hore setšoantšo se kentsoeng e ka ba ntja kapa katse. Moeko, joalo ka 0.5, o behiloe ho etsa sehlopha sa ho qetela: haeba lintlha tsa monyetla li le kholo ho feta kapa li lekana le moeli, setšoantšo se khetholloa e le ntja, ho seng joalo se khetholloa e le katse. Ka kakaretso, marang-rang ana a marang-rang a 3 a nka litšoantšo tsa lintja le likatse tse seng li entsoe esale pele, a sebetsana le tlhahisoleseding e ka hara lera le patiloeng ka thuso ea tsebetso ea tsebetso, me a hlahisa lintlha tse emelang monyetla oa hore setšoantšo se be sa lihlopha tse peli. Ho tsoa ho lintlha tsena, re ka etsa sehlopha sa rona sa ho qetela re sebelisa moeli. 