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Sentiment Analysis with Recurrent Neural Networks in TensorFlow

Sentiment Analysis with Recurrent Neural Networks in TensorFlow

1425/month
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Self paced
English
Course by
PluralsightCourses from Pluralsight
Certificate awarded
Intermediate
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Checklist

Certification

You will get a certificate on completing this course.

University

This course is not affiliated with any university.

Price

This course costs very less.

Edvicer's Rewards

You can get a cashback of ₹ 100 on buying this course.

Why should you choose this course?

Description

Recurrent neural networks (RNNs) are ideal for considering sequences of data. You'll explore how word embeddings are used for sentiment analysis using neural networks. You'll be able to understand and implement word embedding algorithms to generate numeric representations of text, and build a basic classification model. Start learning!

Syllabus

Course Overview
Classification as a Machine Learning Problem
Prerequisites and Software
A Rule-based System for Sentiment Analysis
An Introduction to Neural Networks
One-hot Encoding
Frequency-based Embeddings
Prediction-based Embeddings
Introducing Word2Vec
Overview
Maximum Likelihood Estimation
The Continuous Bag of Words Neural Network
The Skip-gram Neural Network
Useful Python Packages
Demo: Download Data and Extract Words
Demo: Build and Prepare Dataset
Demo: Generate Training Batches
Demo: Contruct the Neural Network
Demo: Train the Neural Network
Noise Contrastive Estimators to Measure Loss
Demo: Implementing Noise Contrastive Estimation
Summary
Text as Sequential Data
The Recurrent Neuron
Input Sequence as a Time Step
Back Propagation Through Time
Long Term Memory
The LSTM Cell
Naive Bayes Intuition
Demo: Implementing Naive Bayes as a Baseline
Drawbacks of Naive Bayes
Demo: Data Preparation for Classification Using RNNs
Demo: Build and Run the Neural Network
Advantages of RNNs for Sentiment Analysis
Demo: Use Pre-trained GloVe Embeddings for Classification
Summary and Further Learning

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