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Great Deep Learning Tutorials for Natural Language Processing (NLP)

Curates 200+ links to deep learning NLP tutorials, papers, libraries, and datasets, organised by topic.

May 2, 2026
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What this file does

Curates 200+ links to deep learning NLP tutorials, papers, libraries, and datasets, organised by topic.

When to use it

  • Starting a new NLP project and need a broad survey of available resources
  • Looking for Persian NLP libraries and datasets
  • Researching specific topics like transformers, RLHF, or LLMs
  • Finding open-source LLMs and their implementations

Assumes this stack

PythonPyTorchTensorFlowKerasspaCyHugging Face Transformers

Great Deep Learning Tutorials for Natural Language Processing (NLP)

A Great Collection of Deep Learning Tutorials and Repositories for Natural Language Processing (NLP)

General:

General Persian based libraries & Data Sets:

Text Representation:

Self-Supervised Learning in NLP:

RNN, LSTM, and GRU:

Transformers:

Reinforcement Learning from Human Feedback (RLHF):

Tokenizer Notes:

Large Language Models (LLMs):

Merge LLMs:

LLaMA2 Related Links:

LLaMA3 Related Links:

DeepSeek Models Related Links:

Phi-3 Related Links:

Mistral & Mixtral Models Related Links:

Yi Models:

Qwen Models:

DeepSeek Models:

Gemma LLM Related Links (by Google):

Jamba (SSM-Transformer Model):

1-bit LLMs:

Long Context Window LLMs (e.g., 100K Tokens LLMs):

Small Language Models (SLMs):

Frameworks for Training & Using Large Language Models (LLMs):

Notes and Codes for Training and fine-tuning LLMs:

Reflection-Tuning of LLMs:

Memory Layer for LLMs:

LLMs for Coding:

LLMs as Front-End Engineers:

LLMs Courses & Tutorials:

LLMs Ranking:

Building NLP Applications Powered by LLMs (Different Methods for Augmenting Knowledge to LLMs (or Retrieval-Augmented Generation (RAG) applications)):

Graph RAG & Its Related Data Bases:

Cache-Augmented Generation (CAG):

Vector Database Libraries:

Great Embedding Models for Search (for Augmenting External Knowledge into ChatBot Vector DB) [Retrieval Augmented Generation (RAG)]:

Prevent Hallucinations from LLMs & Controling their outputs:

Training & Using Large Language Models (LLMs) on Low Resource Machines:

Productionizing LLMs:

LLMs on Mobile Devices:

LLM Applications & APIs:

Natural Language to SQL:

Prompt Engineering:

LLM-based Recommender Systems:

LLMs for Tabular Data:

LLMs as Classifiers (finetuning LLMs for classification):

LLM Data Sets:

LLM based Agents:

Structured Output in LLMs:

Deploying LLMs:

LLM Engineering:

External Tools that Useful for LLMs:

Notes about Cost & Price of Training and Using LLMs:

Excellent & Easy to Learn Resources for Learning Transformers:

Persian based Transformer Models:

Transfer Learning with Transformers:

Siamese Netowrks and Dual BERT for Multi Text Classification:

Attention Mechanism:

Sequence Modeling:

Text Summarization:

Language Model:

Text & Document Classification:

Topic Modeling:

Sentiment Analysis:

Co-Reference Resolution:

Imbalance Handling in NLP:

Information Retrieval:

Distance Measures:

Text-based Emotion Recognition:

Machine Translation:

Chatbot:

Chatbot & LLMs Evaluation Metrics:

OpenAI ChatGPT & Its Applications:

OpenAI Learning to Reason & O1 Models:

Google Bard & Gemini:

Anthropic Claude:

How do LLMs think?

NLP Programming Notes:

Data Annotation Tools:

Dataset Creator Tools:

NLP Courses:

Other NLP Topics & miscellaneous:

What's inside

10 topic sections: General, Persian resources, Text Representation, Self-Supervised Learning, RNN/LSTM/GRU, Transformers, RLHF, Tokenizers, LLMs, plus sub-sections.

Change this for your project

  • Replace ahkarami/Great-Deep-Learning-Tutorials with your own repository name if forking
  • Replace https://github.com/ahkarami/... URLs with your own curated links if maintaining a fork

Where it goes

Reference documentation for a retrieval pipeline. Keep with the ingestion or retrieval code it describes.

Worth borrowing

  • Organising resources by topic and subtopic with quality annotations like [_Excellent_] and [Great]
  • Including both English and language-specific (Persian) resources in the same directory
  • Linking to both papers and their implementations (e.g., GitHub repos, blog posts, LinkedIn posts)

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