পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| মাল্টি-লেয়ার পারসেপ্ট্রন (MLP)× | পুনরাবৃত্ত নিউরাল নেটওয়ার্ক× | |
|---|---|---|
| ক্ষেত্র≠ | যন্ত্র শিখন | গভীর শিখন |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 1986 | 1986–1990 |
| প্রবর্তক≠ | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. | Rumelhart, D. E.; Elman, J. L. |
| ধরন≠ | Feedforward neural network (supervised learning) | Sequential neural network |
| মৌলিক উৎস≠ | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| অপর নাম≠ | MLP, feedforward neural network, fully connected neural network, artificial neural network | RNN, Elman network, Jordan network, simple recurrent network |
| সম্পর্কিত≠ | 4 | 3 |
| সারসংক্ষেপ≠ | The Multi-layer Perceptron (MLP) is a feedforward neural network architecture trained by backpropagation, formalised by Rumelhart, Hinton, and Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons with nonlinear activation functions, and an output layer, the MLP can approximate any continuous function to arbitrary accuracy and serves as the conceptual bridge between classical machine learning and modern deep learning. | A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models. |
| ScholarGateডেটাসেট ↗ |
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