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- Author: Oswald Campesato
- Publisher: Mercury Learning and Information
- ISBN-10: 1683926188
- ISBN-13: 9781683926184
- Format: 21.1 x 28.2 x 3.1 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Natural Language Processing and Machine Learning for Developers (e-book) (used book) | bookbook.eu
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Description
This book is for developers who are looking for an introduction to basic concepts in NLP and machine learning. Numerous code samples and listings are included to support myriad topics. The first two chapters contain introductory material for NumPy and Pandas, followed by chapters on NLP concepts, algorithms and toolkits, machine learning, and NLP applications. The final chapters include examples of NLP tasks using TF2 and Keras, the Transformer architecture, BERT-based models, and the GPT family of models. The appendices contain introductory material (including Python code samples) for various topics, including data and statistics, Python3, regular expressions, Keras, TF2, Matplotlib and Seaborn. Companion files with source code and figures are included.
FEATURES:- Covers extensive topics related to natural language processing and machine learning
- Includes separate appendices on data and statistics, regular expressions, data visualization, Python, Keras, TF2, and more
- Features companion files with source code and color figures from the book.
EXTRA 10 % discount with code: EXTRA
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- Author: Oswald Campesato
- Publisher: Mercury Learning and Information
- ISBN-10: 1683926188
- ISBN-13: 9781683926184
- Format: 21.1 x 28.2 x 3.1 cm, minkšti viršeliai
- Language: English English
This book is for developers who are looking for an introduction to basic concepts in NLP and machine learning. Numerous code samples and listings are included to support myriad topics. The first two chapters contain introductory material for NumPy and Pandas, followed by chapters on NLP concepts, algorithms and toolkits, machine learning, and NLP applications. The final chapters include examples of NLP tasks using TF2 and Keras, the Transformer architecture, BERT-based models, and the GPT family of models. The appendices contain introductory material (including Python code samples) for various topics, including data and statistics, Python3, regular expressions, Keras, TF2, Matplotlib and Seaborn. Companion files with source code and figures are included.
FEATURES:- Covers extensive topics related to natural language processing and machine learning
- Includes separate appendices on data and statistics, regular expressions, data visualization, Python, Keras, TF2, and more
- Features companion files with source code and color figures from the book.
Reviews