11 Years Manufacturer EN14399-4 and 8 System HV Structural nuts Export to Turin
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EN14399-4 and 8 System HV Structural nuts for High-Strength Structural bolting Dimension Standard: EN14399-4 and 8 Metric Size: M12-M36 Material Grade: ISO 898-2 class 10 Finish: Plain, Black Oxide, Zinc Plated, Hot Dipped Galvanized, etc. Packing: Bulk about 25 kgs each carton, 36 cartons each pallet Advantage: High Quality, Competitive Price, Timely Delivery,Technical Support, Supply Test Reports Please feel free to contact us for more details.
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11 Years Manufacturer EN14399-4 and 8 System HV Structural nuts Export to Turin Detail:
EN14399-4 and 8 System HV Structural nuts for High-Strength Structural bolting
Dimension Standard: EN14399-4 and 8
Metric Size: M12-M36
Material Grade: ISO 898-2 class 10
Finish: Plain, Black Oxide, Zinc Plated, Hot Dipped Galvanized, etc.
Packing: Bulk about 25 kgs each carton, 36 cartons each pallet
Advantage: High Quality, Competitive Price, Timely Delivery,Technical Support, Supply Test Reports
Please feel free to contact us for more details.
Product detail pictures:
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Lecture 1 introduces the concept of Natural Language Processing (NLP) and the problems NLP faces today. The concept of representing words as numeric vectors is then introduced, and popular approaches to designing word vectors are discussed.
Key phrases: Natural Language Processing. Word Vectors. Singular Value Decomposition. Skip-gram. Continuous Bag of Words (CBOW). Negative Sampling. Hierarchical Softmax. Word2Vec.
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Natural Language Processing with Deep Learning
Instructors:
- Chris Manning
- Richard Socher
Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component.
For additional learning opportunities please visit:
https://stanfordonline.stanford.edu/
