Sentiment Analysis with Deep Learning using BERT
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12,411 人已注册
Preprocess and clean data for BERT Classification
Load in pretrained BERT with custom output layer
Train and evaluate finetuned BERT architecture on your own problem statement
12,411 人已注册
Preprocess and clean data for BERT Classification
Load in pretrained BERT with custom output layer
Train and evaluate finetuned BERT architecture on your own problem statement
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.
Natural Language Processing
Deep Learning
Machine Learning
Sentiment Analysis
BERT
在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:
Introduction to BERT and the problem at hand
Exploratory Data Analysis and Preprocessing
Training/Validation Split
Loading Tokenizer and Encoding our Data
Setting up BERT Pretrained Model
Creating Data Loaders
Setting Up Optimizer and Scheduler
Defining our Performance Metrics
Creating our Training Loop
Loading and Evaluating our Model
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由 SN 提供
Apr 29, 2021Thanks, Ari Anastassiou for the wonderful tutorial. Hoping you do a complete course on NLP using BERT soon.
由 RT 提供
Jul 18, 2020Cool tutorial. It's more clear now how to work with BERT
由 DB 提供
Nov 17, 2021The course covered the material well. Helpful to do the exercises side by side. I feel the course does require prior knowledge of pyTorch and BERT also.
由 RK 提供
Jun 29, 2020Required detail explanation and faculy support for error soliving and explroing alternative
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