Palash Goyal (26 results)

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Paperback or Softback. Condition: New. Deep Learning for Natural Language Processing: Creating Neural Networks with Python. Book.

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Condition: New. 2018. 1st ed. Paperback. . . . . .

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Paperback. Condition: Brand New. 277 pages. 9.00x6.00x0.75 inches. In Stock.

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Paperback. Condition: new. Paperback. 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.Youll 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.What You Will LearnGain the fundamentals of deep learning and its mathematical prerequisitesDiscover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classificationWho This Book Is ForSoftware developers who are curious to try out deep learning with NLP. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Taschenbuch. Condition: Neu. Deep Learning for Natural Language Processing | Creating Neural Networks with Python | Palash Goyal (u. a.) | Taschenbuch | xvii | Englisch | 2018 | Apress | EAN 9781484236840 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Paperback. Condition: new. Paperback. 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.Youll 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.What You Will LearnGain the fundamentals of deep learning and its mathematical prerequisitesDiscover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classificationWho This Book Is ForSoftware developers who are curious to try out deep learning with NLP. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2019-03-01 Pages: 212 Publisher: Mechanical Industry Press This book transitions from theory to practice in a step-by-step manner. starting with the basics. then the basic math. and finally the implementation of the relevant examples. The first three chapters introduce the basics of NLP. starting with the usual Python libraries. then the word vector representation. and then the advanced calculation.

Paperback. Condition: Brand New. 277 pages. 9.00x6.00x0.75 inches. In Stock. This item is printed on demand.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 not Elektronisches Buch and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisitesDiscover deep learning frameworks in PythonDevelop a chatbotImplement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP. 296 pp. Englisch.

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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Discover and develop your own deep learning networks by solving the puzzle of dropout, pooling, and normalization layersGet an exciting introduction to reinforcement learning and how to make use of context specific behavio.

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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 not Elektronisches Buch and scripts, which allow you to try out the examples and extend them in interesting ways.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 296 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 not Elektronisches Buch and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisitesDiscover deep learning frameworks in PythonDevelop a chatbotImplement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.