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Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks

sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results;

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R Deep Learning Projects

-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning;

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Mission Oriented Sensor Networks and Systems Art and Science

processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning;

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Multimodal Sentiment Analysis

analysis by merging linguistics with machine learning. Fusing textual information with audio and visual cues is found to be extremely useful which;

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Natural Language Processing Recipes

modeling, text summarization, text generation, entity extraction, and sentiment analysis. Natural Language Processing Recipes starts by offering;

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Handbook of Deep Learning Applications

processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning;

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Visual Analysis of Behaviour

This book presents a comprehensive treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives;

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Sentiment Analysis in Social Networks

The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as;

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Sentimental Analysis and Deep Learning

This book gathers selected papers presented at the International Conference on Sentimental Analysis and Deep Learning (ICSADL 2021;

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Deep Learning Based Approaches for Sentiment Analysis

This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has;

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Deep Learning in Mining of Visual Content

This book provides the reader with the fundamental knowledge in the area of deep learning with application to visual content mining. The;

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Computational Methods for Deep Learning

text/reference, but also in relevant deep learning journal articles and conference papers. This textbook/guide is aimed at Computer Science;

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Computational Methods for Deep Learning

text/reference, but also in relevant deep learning journal articles and conference papers. This textbook/guide is aimed at Computer Science;

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Natural Language Processing Recipes

, text summarization, sentiment analysis, information retrieval, and many more applications of NLP. The book begins with text data collection;

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Text Analytics with Python

sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move;

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Text Analytics with Python

sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move;

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Deep Learning for Data Analytics

analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning;

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Machine Learning Using TensorFlow Cookbook

models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google's machine;

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Advanced Deep Learning with R

networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using;

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Data Mining

construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally;

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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured;

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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured;

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TensorFlow Machine Learning Cookbook

NLP and sentiment analysis to your data * Master CNN and RNN through practical recipes * Take TensorFlow into production In Detail;

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Fundamentals Of Deep Learning

the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you;

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TensorFlow Machine Learning Cookbook

evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs;

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Practical TensorFlow.js

, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also;

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Practical TensorFlow.js

, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also;

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