Deep Learning for Natural Language Processing

Creating Neural Networks with Python

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Éditeur :

Apress

Paru le : 2018-06-26

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models.You’ll start by covering the mathematical prerequisites and t...
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Éditeur

Collection
n.c

Parution
2018-06-26

Pages
277 pages

EAN papier
9781484236840

Auteur(s) du livre


Palash Goyal works as Senior Data Scientist, and is currently working with the applications of Data Science and Deep Learning in Online Marketing domain. He studied Mathematics and Computing from IIT-Guwahati, and proceeded to work in a fast, upscale environment.He holds wide experience in E-Commerce, Travel, Insurance, and Banking industries. Passionate about mathematics and Finance, in his free time he manages his portfolio of multiple Cryptocurrencies and latest ICOs using Deep Learning and Reinforcement Learning techniques for price prediction and portfolio management.He keeps himself in touch with the latest trends in the Data Science field and pen it down on his personal blog and digs articles related to Smart Farming in left over time. Sumit Pandey is a graduate from IIT Kharagpur. He worked for about a year with AXA Business services as a Data Science Consultant. He is currently engaged in launching his own venture.Karan Jain is Product Analyst at Sigtuple , where he works on cutting edge AI driven diagnostic products . Before which he worked as a Data Scientist at Vitrana Inc , a healthcare solutions company.He enjoys working in fast culture and data-first start ups. In his leisure time he deeps dive into Genomics sciences, BCI interfaces , Optogenetics . He recently developed interest in POC devices and Nano tech for further portable diagnosis. He has healthy network of 3000+ followers on linkedin. 

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EAN PDF
9781484236857
Prix
66,05 €
Nombre pages copiables
2
Nombre pages imprimables
27
Taille du fichier
7557 Ko

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