Guide to Getting Started with Deep Learning, NLP, and Large Models: A Friendly Manual with Diagrams

This open-source project dl-nlp-llm-for-dummies is maintained by mahdinaser and was published on GitHub (May 26, 2026). It provides a systematic learning path for beginners in deep learning, natural language processing (NLP), and large language models (LLMs). The project uses diagram-driven learning to lower the barrier for complex concepts, positioning itself as a friendly domain guide suitable for beginners without a strong mathematical background.

Deep learning, NLP, and LLMs are popular directions in AI, but their knowledge systems are complex and concepts are abstract, making the entry barrier high. Existing tutorials are either too theoretical or skip directly to code without concept explanations. This project addresses this pain point and positions itself as a 'friendly domain guide' for beginners who want to systematically understand these fields but lack a strong mathematical background.

The core feature of the project is diagram-driven learning, using visual charts to explain abstract concepts such as backpropagation and attention mechanisms. The content covers three main themes:

1. Deep learning basics: Neural network structures, CNN, RNN, optimization algorithms, etc.
2. NLP: Text preprocessing, word embeddings (Word2Vec/GloVe), sequence labeling, etc.
3. Large language models: Pre-trained models (BERT/GPT), architecture evolution, emergent capabilities, etc. The learning path is progressive: Concept first → Example assistance → Diagram reinforcement → Association and integration.

1. Lower cognitive barriers: Adopts the 'understand first, then dive deep' strategy—build an overall framework first before supplementing mathematical details.
2. Establish a knowledge map: Clear theme division and progressive relationships solve problems like 'alternating between learning and forgetting' and 'not knowing the next learning direction'.
3. Bridge theory and practice: Explains concepts while focusing on practical applications, balancing 'why' and 'how'.

As an open-source resource, it has:

1. Knowledge democratization: Integrates scattered high-quality AI educational resources and lowers the access barrier.
2. Supplement to Chinese communities: Although it is in English, its friendly diagrams can supplement Chinese AI educational resources.
3. Potential for continuous updates: The GitHub open-source model allows community contributions, enabling content updates as technology evolves.

Suggestions for learners:

1. Read through to build a framework: Quickly browse all content to form an overall understanding.
2. Read in depth with practice: Dive into topics of interest and combine with code practice.
3. Use diagrams as a review tool: Use diagrams to quickly review core concepts.
4. Extend reading: Take this project as a starting point to read cited papers and advanced resources in depth.

This project is a beneficial attempt at AI educational resources. It uses diagrams to lower the barrier for abstract concepts and structured organization to help build a knowledge map. In today's era of rapid AI technology iteration, such friendly entry resources are of great significance for cultivating AI talents and promoting technology popularization. It is a guide worth collecting and referencing for learners in the fields of deep learning, NLP, and LLMs.