Sentiment Analysis with Deep Learning using BERT
Key Takeaways
Fine-tunes a BERT model for sentiment analysis using PyTorch
Original Description
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.
AI explanation not available for this lesson yet
This lesson is still being prepared for the AI tutor. In the meantime, explore lessons that are ready.
Browse explainer-ready lessons →
More on: Fine-tuning LLMs
View skill →Related Reads
📰
📰
📰
📰
AI/ML Under the Hood — Part 29: CNN Breaking News: Proximity Matters
Medium · Deep Learning
Deep Learning Scientists — Claude Cowork: The Deep Learning Scientist’s New Lab Partner
Medium · Data Science
Why Qwen3.8 27B Looked Brilliant in Testing but Failed to Ship My AI Newspaper
Medium · Deep Learning
The 91% Model: What August’s Benchmarks Reveal About the Real Cost of Staying at the Frontier
Medium · Deep Learning
🎓
Tutor Explanation
DeepCamp AI