NLP Advanced Overview

Natural Language Processing (NLP) has seen significant advancements in recent years. This section provides an overview of some of the key developments and concepts in the field.

Key Concepts

• Machine Learning: A subset of AI that enables machines to learn from data and make decisions or predictions based on that data.
• Deep Learning: A subset of machine learning that involves neural networks with many layers.
• Transfer Learning: A technique where a model trained on one task is fine-tuned on another related task.

Applications

• Sentiment Analysis: Analyzing the sentiment of a piece of text, such as a review or social media post.
• Text Classification: Categorizing text into predefined categories, such as spam or ham.
• Machine Translation: Translating text from one language to another.

Tools and Libraries

• TensorFlow: An open-source machine learning framework developed by Google.
• PyTorch: An open-source machine learning library developed by Facebook's AI Research lab.
• Scikit-learn: A Python-based library for machine learning in Python.

For more information on these tools and libraries, visit our Machine Learning Resources.

Resources

• Natural Language Toolkit (NLTK)
• Stanford NLP Group

NLP Architecture