courseraАнализ данных

Sentiment Analysis with Deep Learning using BERT

Курс от Coursera

Средний≈ 2.3 чАнглийский

О курсе

In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Навыки, которые вы освоите

Model Training Exploratory Data Analysis PyTorch (Machine Learning Library)Machine Learning Data Preprocessing Deep Learning Model Optimization Test Data Natural Language Processing Fine-tuning Model Evaluation Data Cleansing Large Language Modeling Transfer Learning

Программа курса

1 модулей · 4 учебных материалов

01Sentiment Analysis with Deep Learning using BERT4 материалов

Sentiment Analysis with Deep Learning using BERT

Project-based Course OverviewЧтение Sentiment Analysis with Deep Learning using BERTЛабораторная Graded Quiz: Test your Project understandingЗадание

Учитесь у экспертов

Ari Anastassiou

Преподаватель курса