Linganisha mbinu

	Kielelezo cha Tabaka Nyingi cha Njia Nyingi	Multilayer Perceptron Iliyoendeshwa Vizuri
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2011 (multimodal extension); 1986 (MLP backpropagation)	1986 (MLP); fine-tuning practice formalised c. 2014
Mwanzilishi	Ngiam et al. / Rumelhart, Hinton & Williams (MLP foundations)	Rumelhart, Hinton & Williams (MLP); Yosinski et al. (fine-tuning analysis)
Aina	Feedforward neural network with multi-stream fusion	Supervised deep learning with pre-trained weight initialisation
Chanzo asilia	Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 689–696.	Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
Majina mbadala	MM-MLP, multimodal MLP, multi-input feedforward network, fusion multilayer perceptron	fine-tuned MLP, adapted MLP, domain-adapted multilayer perceptron, MLP fine-tuning
Zinazohusiana	5	4
Muhtasari	A Multimodal Multilayer Perceptron (MM-MLP) is a feedforward neural network that ingests features from two or more heterogeneous input modalities — such as structured tabular data, text embeddings, and image feature vectors — by encoding each stream separately and fusing them into a shared representation before passing it through fully connected layers to produce a classification or regression output.	A Fine-Tuned Multilayer Perceptron starts from weights learned on a source task — or a large general-purpose dataset — and continues training on a smaller target dataset with a reduced learning rate. This reuse of pre-learned representations allows the MLP to converge faster and generalise better than training from scratch, especially when labelled target data is scarce.