Arama Sonuçları - gates features

Computer Systems Digital Design, Fundamentals of Computer Architecture and Assembly Language / Yazar: Elahi, Ata

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Foundations of Embedded Systems Yazar: Barkalov, Alexander, Titarenko, Larysa, Mazurkiewicz, Małgorzata

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Computational Intelligence and Sustainable Systems Intelligence and Sustainable Computing /

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Intelligent Engineering Informatics Proceedings of the 6th International Conference on FICTA /

İçindekiler: “…Application of TF-IDF Feature for Categorizing Documents of Online Bangla Web Text Corpus -- 7. …”
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Computational Intelligence in Data Mining-Volume 1 Proceedings of the International Conference on CIDM, 5-6 December 2015 /

İçindekiler: “…Optimization Approach for Feature Selection and Classification with Support Vector Machine -- Chapter 12. …”
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Information Systems Design and Intelligent Applications Proceedings of Third International Conference INDIA 2016, Volume 3 /

İçindekiler: “…Analysis and Optimization of Feature Extraction Techniques for Content Based Image Retrieval -- Chapter 37. …”
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Deep Learning with TensorFlow and Keras - 3rd Edition : Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. Yazar: Kapoor, Amita

İçindekiler: “…Language model-based embeddings -- Using BERT as a feature extractor -- Summary -- References -- Chapter 5: Recurrent Neural Networks -- The basic RNN cell -- Backpropagation through time (BPTT) -- Vanishing and exploding gradients -- RNN cell variants -- Long short-term memory (LSTM) -- Gated recurrent unit (GRU) -- Peephole LSTM -- RNN variants -- Bidirectional RNNs -- Stateful RNNs -- RNN topologies -- Example ‒ One-to-many - Learning to generate text -- Example ‒ Many-to-one - Sentiment analysis -- Example ‒ Many-to-many - POS tagging -- Encoder-decoder architecture - seq2seq -- Example ‒ seq2seq without attention for machine translation -- Attention mechanism -- Example ‒ seq2seq with attention for machine translation -- Summary -- References -- Chapter 6: Transformers -- Architecture -- Key intuitions -- Positional encoding -- Attention -- Self-attention -- Multi-head (self-)attention -- How to compute attention -- Encoder-decoder architecture -- Residual and normalization layers -- An overview of the transformer architecture -- Training -- Transformers' architectures -- Categories of transformers -- Decoder or autoregressive -- Encoder or autoencoding -- Seq2seq -- Multimodal -- Retrieval -- Attention -- Full versus sparse -- LSH attention -- Local attention -- Pretraining -- Encoder pretraining -- Decoder pretraining -- Encoder-decoder pretraining -- A taxonomy for pretraining tasks -- An overview of popular and well-known models -- BERT -- GPT-2 -- GPT-3 -- Reformer -- BigBird -- Transformer-XL -- XLNet -- RoBERTa -- ALBERT -- StructBERT -- T5 and MUM -- ELECTRA -- DeBERTa -- The Evolved Transformer and MEENA -- LaMDA -- Switch Transformer -- RETRO -- Pathways and PaLM -- Implementation -- Transformer reference implementation: An example of translation -- Hugging Face -- Generating text -- Autoselecting a model and autotokenization.…”
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Proceedings of the International Conference on Data Engineering and Communication Technology ICDECT 2016, Volume 2 /

İçindekiler: “…Performance Analysis of LPC and MFCC Features in Voice Conversion using Artificial Neural Networks -- Chapter 28. …”
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