Websit: https://www.jm-industry.com
4 FINS under the head of timber bolt with grade of ASTM A307 Gr.A and H.D.G or plain finish.
Now I would like to introduce our timber bolt.
The timber bolt are mainly used for Forest products such as wood pallet etc.
The timber bolts we manufactured before are size from 5/8” to 1 1/2”with grade of ASTM A307 Gr.A and H.D.G or plain finish.this is the Hot-Dip Galvanized finished timber bolt , and this is ASTM A307 Gr.A, and this the manufacture head mark “JM”.
And you can find there are 4 FINS under the head of timber bolt, again the timber bolts with two fins is always availiable for you, too.
Shanghai jian & mei is a pofessional manufacturer of bolt products,our products are mainly applied in Structural Steel,Metal Buliding,Oil&Gas,Tower&Pole,Wind,Energy,Mechanical Machine,Automobile,Home Decorating and so on.
We keep full rang sizes.
the Materials could be Carbon Steel, Alloy Steel, Stainless Steel and Brass.
Availiable Grade of bolts could be SAE J429 Gr2, 5, 8,ASTM A325,A307,A490,ASTM A193 B7 and class 4.8 6.8 8.8,10.9,12.9,
The finish could be Zinc Plated,yellow zinc plated,Black oxide, H.D.G and so on.
we export quality timber bolts to American market for more than 8 years and enjoyed very good reputation,we are your reliable fastener supplier!
Wecome to contact us any time!
Shanghai Jian & Mei Industry and Trade Co., Ltd
Rm.27202, No.295 South Lingyan Road, Pudong,Shanghai,P.R.C, Code:200126
Tel: 0086-21-33328690-8058
Fax: 0086-21-68413263
Web：www.jm-industry.com
Contact: Mrs.Xarella Huang
Email: Jminfo@jm-industry.com
Websit: https://www.jm-industry.com
Lecture 4 introduces single and multilayer neural networks, and how they can be used for classification purposes.
Key phrases: Neural networks. Forward computation. Backward propagation. Neuron Units. Max-margin Loss. Gradient checks. Xavier parameter initialization. Learning rates. Adagrad.
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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/