<prompt> Phapano ke efe lipakeng tsa Mochini oa Vector ea Tšehetso SVM le Neural Network?nan  <response>: Instruction in English: What is the difference between a Support Vector Machine SVM and a Neural Network? . Response in English: A Support Vector Machine SVM and a Neural Network are both supervised machine learning algorithms used for classification and regression tasks. However, there are several key differences between the two:1. Architecture: An SVM is a linear classifier that finds a hyperplane that separates samples of different classes with the largest possible margin. On the other hand, a Neural Network is composed of layers of interconnected nodes or neurons that can model both linear and non-linear relationships between input and output.2. Decision Boundaries: SVMs find linear decision boundaries by default, but they can also find non-linear decision boundaries by using kernel functions. Neural Networks can find both linear and non-linear decision boundaries by using different activation functions in the nodes.3. Learning: An SVM is trained by finding the maximum margin hyperplane. This is done by solving a quadratic optimization problem. In contrast, a Neural Network is trained by adjusting the weights between the nodes using gradient descent or other optimization techniques.4. Robustness: SVMs are less sensitive to the number of features in the dataset and can perform well even with smaller datasets. Neural Networks, on the other hand, require larger datasets and more training time to achieve good performance, but they can handle high-dimensional datasets more effectively.In summary, SVMs and Neural Networks are both powerful algorithms, but they have different strengths and weaknesses. The choice between the two depends on the specific problem at hand, as well as the nature of the data and the availability of computing resources. . Response in Sesotho: Mochini oa Tsehetso ea Vector SVM le Neural Network ka bobeli ke li-algorithms tsa ho ithuta tsa mochini tse hlokometsoeng tse sebelisetsoang ho hlophisa le ho fokotsa mesebetsi. Leha ho le joalo, ho na le liphapang tse maloa tsa bohlokoa lipakeng tsa tse peli: 1. Architecture: SVM ke linear classifier e fumanang hyperplane e arolang lisampole tsa lihlopha tse fapaneng tse nang le moeli o moholo ka ho fetisisa. Ka lehlakoreng le leng, Neural Network e entsoe ka likarolo tsa li-node tse hokahaneng kapa li-neuron tse ka etsang mohlala oa likamano tse sa tsitsang le tse se nang moeli lipakeng tsa ho kenya le ho tsoa. 2. Meeli ea Liqeto: Li-SVM li fumana meeli ea liqeto ka mokhoa o ikhethileng, empa li ka boela tsa fumana meeli ea liqeto tse se nang moeli ka ho sebelisa mesebetsi ea kernel. Neural Networks e ka fumana meeli ea liqeto tse nang le mela le e seng moeli ka ho sebelisa mesebetsi e fapaneng ea tsebetso ho li-node. 3. Ho Ithuta: SVM e koetlisoa ka ho fumana li-hyperplane tse holimo ka ho fetisisa. Sena se etsoa ka ho rarolla bothata ba quadratic optimization. Ka lehlakoreng le leng, Neural Network e koetlisoa ka ho lokisa litekanyo lipakeng tsa li-node ho sebelisa ho theoha ha sekhahla kapa mekhoa e meng ea ho ntlafatsa. 4. Matla: Li-SVM ha li na kutloelo-bohloko ho palo ea likarolo tsa dataset me li ka sebetsa hantle esita le ka li-dataset tse nyane. Neural Networks, ka lehlakoreng le leng, e hloka li-datasets tse kholoanyane le nako e eketsehileng ea koetliso ho finyella tsebetso e ntle, empa li ka sebetsana le li-dataset tsa boemo bo phahameng ka katleho. Ka kakaretso, li-SVM le Neural Networks ka bobeli ke li-algorithms tse matla, empa li na le matla le mefokolo e fapaneng. Khetho lipakeng tsa tse peli e ipapisitse le bothata bo itseng bo teng, hammoho le mofuta oa data le boteng ba lisebelisoa tsa komporo. 