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Sentence Parsing for Afan Oromo using Supervised Learning Techniques

user. The area of NLP is broad and wide in scope. Hence, this book highlights syntactic disambiguation and parsing using statistical methods;

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The effects of Mother Tongue on second language learning

The purpose of study was designed to examine the impacts of mother tongue(Afan Oromo) and other school related factors on student learning;

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Semi-Supervised Dependency Parsing

data and have shown their advantages in the context of dependency parsing for many languages. Various semi-supervised dependency parsing;

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TensorFlow 2.0 Quick Start Guide

your own models for effective prediction, using high-level Keras API Perform supervised and unsupervised machine learning and learn advanced;

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Supervised and Unsupervised Learning for Data Science

classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised;

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Machine Learning Foundations

techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides;

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Applied Supervised Learning with Python

Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some;

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Semantic Role Labeling

has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3;

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Advanced Natural Language Processing with TensorFlow 2

complicated tasks Key Features Apply deep learning algorithms and techniques such as BiLSTMS, CRFs, BPE and more using TensorFlow 2 Explore;

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Machine Learning and Data Science Blueprints for Finance From Building Trading Strategies to RoboAdvisors Using Python

over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for;

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Python Natural Language Processing Cookbook

develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you'll also see how to extract;

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Semantic Role Labeling

has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3;

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Grammatical Competence and Parsing Performance

the parser successfully functions the first time around, Pritchett makes a convincing case for a grammar-derived parsing theory.;

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Machine Learning with scikit-learn Quick Start Guide

. Key Features Build your first machine learning model using scikit-learn Train supervised and unsupervised models using popular techniques such;

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Text Analytics with Python

. You'll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised;

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Text Analytics with Python

. Yoüll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Advanced Data Analytics Using Python

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;

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Advanced Data Analytics Using Python

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;

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Applied Supervised Learning with R

DescriptionR provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised;

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Medical Image Recognition, Segmentation and Parsing

This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple;

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Relative-Clause Processing in Korean Adults

crosslinguistic universals of structural properties and the linear order of surface constituents as possible explanations of parsing preferences. Based on;

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Python Machine Learning Cookbook

to supervised, unsupervised and reinforcement learning techniques Find easy-to-follow code solutions for tackling common and not-so-common;

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Sampling Techniques for Supervised or Unsupervised Tasks

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling;

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Sampling Techniques for Supervised or Unsupervised Tasks

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling;

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Neural Network Methods in Natural Language Processing

architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other;

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