Text Analysis With Python A Research Oriented Guide
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Text Analysis with Python
Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features · Introduces the reader to Python programming and data processing · Introduces the reader to the preliminaries of natural language processing (NLP) · Covers data analysis and visualization using predefined python libraries and datasets · Teaches how to write text mining programs in Python · Includes text classification and clustering techniques · Informs the reader about different types of neural networks for text analysis · Includes advanced analytical techniques such as fuzzy logic and deep learning techniques · Explains concepts in a simplified and structured way that is ideal for learners · Includes References for further reading Text Analysis with Python: A Research-Oriented Guide is an ideal guide for students in data science and computer science courses, and for researchers and analysts who want to work on artificial intelligence projects that require the application of text mining and NLP techniques.
Text Analysis with Python: A Research Oriented Guide
Author: Mamta Mittal
language: en
Publisher: Bentham Science Publishers
Release Date: 2022-08-12
Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features · Introduces the reader to Python programming and data processing · Introduces the reader to the preliminaries of natural language processing (NLP) · Covers data analysis and visualization using predefined python libraries and datasets · Teaches how to write text mining programs in Python · Includes text classification and clustering techniques · Informs the reader about different types of neural networks for text analysis · Includes advanced analytical techniques such as fuzzy logic and deep learning techniques · Explains concepts in a simplified and structured way that is ideal for learners · Includes References for further reading Text Analysis with Python: A Research-Oriented Guide is an ideal guide for students in data science and computer science courses, and for researchers and analysts who want to work on artificial intelligence projects that require the application of text mining and NLP techniques.
Deep learning
Author: Dr. Venkata Kiran Kumar Ravi, RaviKumar Bhuvanagiri, Dr. A. Ajmal Khaan, Mrs. S. Jagadeeswari
language: en
Publisher: BR Publications
Release Date: 2025-12-31
Deep learning is a subfield of artificial intelligence that focuses on training multi-layered neural networks to learn patterns from large datasets. It enables machines to automatically extract features from raw data, reducing the need for manual feature engineering. Deep learning models consist of multiple hidden layers that progressively transform inputs into meaningful representations. Techniques such as backpropagation and gradient descent are used to optimize network parameters. Activation functions introduce nonlinearity, allowing models to solve complex problems. Convolutional neural networks handle image data, while recurrent and transformer models process sequential information. Large-scale data and powerful computing resources are essential for effective training. Regularization methods help prevent overfitting and improve generalization. Transfer learning allows reuse of pretrained models for new tasks. Deep learning powers applications like speech recognition, computer vision, and natural language processing. Model performance depends on architecture design and data quality. Ethical considerations and interpretability are increasingly important. Overall, deep learning drives modern intelligent systems and technological innovation.