000			01921nam a22002537a 4500
999			_c3870 _d3870
005			20221122122728.0
008			221122b ||||| |||| 00| 0 eng d
020			_a9781484246016
082			_a006.35 _bGOY
100			_aGoyal, Palash _99091
245			_aDeep learning for natural language processing: _bcreating neural networks with python
250			_a2nd
260			_bApress _aNew York _c2021
300			_axvii, 277 p.
365			_aINR _b829.00
520			_aAbout this book 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 the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways.
650			_aPython (Computer program language) _910211
650			_aNeural networks (Computer science) _92344
650			_aNatural language processing (Computer science) _97016
650			_aMachine learning _92343
700			_aPandey, Sumit _910212
700			_aJain, Karan _910213
942			_2ddc _cBK