This repository provides an 8-week curriculum for learning Natural Language Processing (NLP), aimed at individuals seeking to enter the field through consulting or full-time employment. It offers a structured path covering foundational concepts to advanced deep learning techniques, with a focus on practical application using Python and PyTorch.

How It Works

The curriculum is structured weekly, combining video lectures, reading assignments, and hands-on projects. It progresses from basic NLP terminology and preprocessing to language models, word embeddings (Word2Vec), deep sequence modeling (Seq2Seq, Attention), dialogue systems, and transfer learning with models like BERT and GPT-2. The approach emphasizes practical implementation in PyTorch, with links to external resources for deeper understanding.

Quick Start & Requirements

• Prerequisites: Python, Statistics, Probability, Calculus, Linear Algebra. Links to learning resources are provided.
• Tools: Python, PyTorch, NLTK.
• Study Time: 2-3 hours per day for 8 weeks.

Highlighted Details

• Covers pre-deep learning NLP techniques like HMM and LDA.
• Includes implementation of Word2Vec and dependency parsers.
• Features projects on sequence-to-sequence models for translation and summarization.
• Explores transfer learning with BERT/GPT-2 and their applications.
• Addresses advanced topics like dialogue systems and deep reinforcement learning for NLP.

Maintenance & Community

• The curriculum is associated with Siraj Raval and his YouTube channel.
• A Slack channel (#NLP_curriculum) is available for finding study buddies.

Licensing & Compatibility

• No explicit license is mentioned in the README.

Limitations & Caveats

The repository primarily serves as a curriculum outline and a collection of links to external resources and projects. Users will need to independently set up development environments and integrate the various components. Some linked projects may have their own specific dependencies or maintenance status.