Natural Language Processing From First Principles

Learn Natural Language Processing (NLP) and Deep Learning Fundamentals – No Prior Experience Required!

This course is designed for motivated beginners eager to dive into the world of artificial intelligence and data science. You'll gain a solid understanding of core concepts, with a strong emphasis on practical implementation.

Build Your Own Word Embeddings: Go beyond theory and code your own word embedding vectors from scratch using only Numpy and basic calculus. No complex libraries needed!

Math Made Easy: Don't worry about a strong math background. We include a crash course in essential mathematics, covering differential calculus and linear algebra with a concise overview tailored for NLP. We break down every mathematical derivation step-by-step, fostering a deeper understanding of natural language processing and AI.

Master Research Papers: Stop passively consuming information. Learn a repeatable framework to read, understand, and implement deep learning research papers directly from the source. Discover how the language in research translates to real-world code. This is a crucial skill for any AI practitioner or data scientist.

Focus on Best Practices: We emphasize good coding practices from the start. Learn to write pythonic and extensible code that's easy to scale for production environments.

Key Topics Covered:

• Word Embeddings: Understand the differences between skip-gram and continuous bag of words models.
• Distributional Semantics: Explore how vectors can be used to represent and understand language.
• Word2Vec Gradients: Learn how to derive the Word2Vec gradients.
• Softmax Optimization: Understand why the softmax function can be a performance bottleneck in NLP.
• Small Dataset Strategies: Discover techniques for handling small datasets in natural language processing.
• Negative Sampling: Improve word embedding quality using negative sampling.
• Proper Noun Handling: Learn best practices for dealing with proper nouns in NLP.
• Historical NLP Approaches: Gain insight into the evolution of natural language processing.
• Word Plots: Understand what word plots reveal about how computers interpret language.

What You'll Be Able to Do:

By the end of this course, you'll be able to confidently answer questions like:

• What are the core differences between skip-gram and CBOW?
• How does distributional semantics work?
• How can we represent language using vectors?
• How are Word2Vec gradients calculated?
• Why is softmax slow in NLP applications?
• How to address challenges with limited data in NLP?
• How to enhance word embeddings with negative sampling?
• Best practices for handling proper nouns in NLP?
• What are the historical milestones in NLP?
• What insights can word plots provide?

Fast-Paced, No-Fluff Learning: This course delivers focused content at a brisk pace. It's ideal for motivated beginners who want to gain deep insights into NLP and start implementing research papers independently. You'll avoid relying on simplified explanations and gain the skills to build your own NLP projects.