- Linear algebra - https://colab.research.google.com/github/ageron/handson-ml2/blob/master/math_linear_algebra.ipynb#scrollTo=tpUbZM665T0D
- Attention is all you need - https://arxiv.org/abs/1706.03762
- Prompt engineering - https://arxiv.org/pdf/2406.06608
- Prompting - https://sdk.vercel.ai/prompt
- Prompting - https://www.promptingguide.ai/
- Embeddings - https://pub.aimind.so/llm-embeddings-explained-simply-f7536d3d0e4b
- Prompting (polish) - https://www.promptopedia.pl/prompt/jak-dziala-prompt
- Transformers (PL) - https://youtu.be/M5boVfJXd3Q
- PEFT, LoRA - https://huggingface.co/blog/peft
- Catastrophic forgetting - https://arxiv.org/abs/1312.6211
- ROUGE - https://www.freecodecamp.org/news/what-is-rouge-and-how-it-works-for-evaluation-of-summaries-e059fb8ac840/
- Custom tools in LangChain - https://lancerninja.com/custom-tools-langchain/
- Pinacone - use wit RAG https://lancerninja.com/retrieval-augmentation-using-pinecone/
- LLM from scratch - https://youtu.be/UU1WVnMk4E8?list=PLg9s9Jz3UGc0pD9wljWELchTODHHSQxks
- Elevate Your AI to New Heights with LLM-Powered Multi-Agent Frameworks! - https://medium.com/illuminations-mirror/elevate-your-ai-to-new-heights-with-llm-powered-multi-agent-frameworks-2efc0d2e94a5
- GenAI : Witness Automated Test Cases Generation in Action, let’s build - https://medium.com/@learn-simplified/genai-witness-automated-test-cases-generation-in-action-lets-build-ebc7f5ce0afb
- GenAI on AWS Amazon Q - https://aws.amazon.com/q/
- Agents in LangChain - https://lancerninja.com/agents-in-langchain/
- Agents in Semantic Kernel - https://learn.microsoft.com/en-us/semantic-kernel/concepts/agents?pivots=programming-language-python
- Build LLM from scratch for beginners - https://www.analyticsvidhya.com/blog/2023/07/beginners-guide-to-build-large-language-models-from-scratch/
- Prompting - https://ai.google.dev/gemini-api/docs/prompting-intro
- Test generation - https://spr.com/crafting-test-cases-with-generative-ai/
- Multimodal AI - https://www.datacamp.com/blog/what-is-multimodal-ai?utm_source=google&utm_medium=paid_search&utm_campaignid=19589720821&utm_adgroupid=152984010854&utm_device=c&utm_keyword=&utm_matchtype=&utm_network=g&utm_adpostion=&utm_creative=684592139678&utm_targetid=dsa-2222697810678&utm_loc_interest_ms=&utm_loc_physical_ms=9043207&utm_content=DSA~blog~Artificial-Intelligence&utm_campaign=230119_1-sea~dsa~tofu_2-b2c_3-row-p1_4-prc_5-na_6-na_7-le_8-pdsh-go_9-nb-e_10-na_11-na-june24&gad_source=1&gclid=CjwKCAjwkJm0BhBxEiwAwT1AXAaEDujS3NilLy5Ws_2k8Sn1JUFjradzVgYBAc3Gkb_Ehx5efQMxXhoC_9sQAvD_BwE
- Top 100 OSS - https://www.benchcouncil.org/evaluation/opencs/annual.html
- Intuitive introduction to Embeddings - https://stackoverflow.blog/2023/11/09/an-intuitive-introduction-to-text-embeddings/
- Guide to llama.cpp - https://pyimagesearch.com/2024/08/26/llama-cpp-the-ultimate-guide-to-efficient-llm-inference-and-applications/
- llama.cpp: Conversion, Quantization, & Merging - https://rentry.org/llama-cpp-conversions#converting-loras-to-gguf
- llama.cpp: Finetune LoRA on CPU using - https://rentry.org/cpu-lora
- OpenAI platform - https://platform.openai.com
- Autogen - https://github.com/microsoft/autogen
- MetaGPT - https://github.com/geekan/MetaGPT
- Semantic Kernel docs - https://learn.microsoft.com/en-us/semantic-kernel/overview/
- Semantic Kernel - https://github.com/microsoft/semantic-kernel/tree/main
- MemGPT, extending history context in talking to LLMs - https://youtu.be/nQmZmFERmrg
- AI Agentic Patterns with Autogen - Free Course - https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen/?utm_campaign=autogen-launch&utm_medium=video&utm_source=youtube&utm_content=teaser
- Chainlit: Building conversational AI (github) - https://github.com/Chainlit/chainlit
- Chainlit - https://docs.chainlit.io/get-started/overview
- Mistral AI Developer platform - https://console.mistral.ai/
- llama.cpp with C++ API - https://mutable.ai/ggerganov/llama.cpp
- llama.cpp (github) - https://github.com/ggerganov/llama.cpp
- gemma.cpp - https://ai.google.dev/gemma/docs/gemma_cpp
- llama2 - https://github.com/karpathy/llama2.c
- Huggingface Hub - https://huggingface.co/
- Tokenizer (OpenAI) - https://platform.openai.com/tokenizer
- Streamlit, webapp creation tool (nice for playing with GenAI) - https://streamlit.io/
- Chroma: AI-native open-source vector database - https://docs.trychroma.com/getting-started
- PettingZo: Python library for conducting research in multi-agent reinforcement learning - https://github.com/Farama-Foundation/PettingZoo
- Gymnasium: library for developing and comparing reinforcement learning algorithms https://github.com/Farama-Foundation/Gymnasium
- LLaVA - https://www.microsoft.com/en-us/research/project/llava-large-language-and-vision-assistant/
- Weights & Biases - https://wandb.ai/site
- Microsoft UFO - https://www.microsoft.com/en-us/research/publication/ufo-a-ui-focused-agent-for-windows-os-interaction/
- Mistral-finetuning - https://github.com/mistralai/mistral-finetune
- Axolotl finetuning - https://github.com/axolotl-ai-cloud/axolotl
- AnythingLLM for running LLMs - https://anythingllm.com/
- LM Studio for running LLMs - https://lmstudio.ai/
- UFO - Windows OS manage - https://github.com/microsoft/UFO
- Web crawler for data collection - https://github.com/unclecode/crawl4ai
- LLMs benchmarking - https://super.gluebenchmark.com/
- Compute framework - https://www.ray.io/
- Semantic router - https://www.aurelio.ai/semantic-router
- Semantic router (source) - https://github.com/aurelio-labs/semantic-router
- LangFlow, fast prototyping - https://github.com/langflow-ai/langflow
- Loading PDF data for RAG, LLM - https://pymupdf.readthedocs.io/en/latest/rag.html
- How Transformers and Large Language Models (LLMs) Work — A Comprehensive Guide Using BERT, GPT, and T5 - https://blog.gopenai.com/how-transformers-and-large-language-models-llms-work-3f20bb41c1ff
- Transformers Explained Visually (Part 1): Overview of Functionality - https://towardsdatascience.com/transformers-explained-visually-part-1-overview-of-functionality-95a6dd460452
- Transformers Explained Visually (Part 2): How it works, step-by-step - https://towardsdatascience.com/transformers-explained-visually-part-2-how-it-works-step-by-step-b49fa4a64f34
- Transformers Explained Visually (Part 3): Multi-head Attention, deep dive - https://towardsdatascience.com/transformers-explained-visually-part-3-multi-head-attention-deep-dive-1c1ff1024853
- https://www.pluralsight.com/resources/blog/data/what-are-transformers-generative-ai
- https://huggingface.co/docs/transformers/en/index
- https://huggingface.co/tasks/text-generation
- https://huggingface.co/docs/transformers/en/main_classes/text_generation
- T5 explained - https://cameronrwolfe.substack.com/p/t5-text-to-text-transformers-part
- T5 Transformer model - https://wandb.ai/mukilan/T5_transformer/reports/Exploring-Google-s-T5-Text-To-Text-Transformer-Model--VmlldzoyNjkzOTE2
- What are Q, K, V ? - https://stats.stackexchange.com/questions/421935/what-exactly-are-keys-queries-and-values-in-attention-mechanisms
- Illustrated transformers - https://jalammar.github.io/illustrated-transformer/
- Deploying LLM with FastChat - https://medium.com/@tubelwj/deploying-llm-with-fastchat-2cb3a2189a9e
- Trick to use Semantic Kernel with local LLM - https://www.developerscantina.com/p/semantic-kernel-open-source-llms/
- LLaVa - https://llava-vl.github.io/
- RAG in TorchServe - https://pytorch.org/serve/enhancing_llm_serving_compile_rag.html
- Vector DBs comparison - https://superlinked.com/vector-db-comparison
- Step-back paper - https://arxiv.org/pdf/2310.06117
- RAPTOR - https://arxiv.org/abs/2401.18059
- RAG-Fusion - https://github.com/Raudaschl/rag-fusion
- RAG introduction by LangChain - https://python.langchain.com/v0.2/docs/tutorials/rag/
- Query construction by LangChain - https://blog.langchain.dev/query-construction/
- Query analysis by LangChain - https://python.langchain.com/docs/tutorials/query_analysis/
- Evaluation - let's not forget about testing! - https://hamel.dev/blog/posts/evals/
- Corrective-RAG paper - https://arxiv.org/abs/2401.15884
- ColBERT paper - https://arxiv.org/abs/2004.12832
- ColBERT - github - https://github.com/stanford-futuredata/ColBERT
- Chunking tool - https://chunkviz.up.railway.app/
- Adaptive RAG paper - https://arxiv.org/abs/2403.14403
- Multi-agent systems - https://tech.bertelsmann.com/en/blog/articles/harnessing-the-power-of-multi-agent-systems-for-generative-ai-part-1
- Autogen Multi-agent framework - https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat/#supporting-diverse-conversation-patterns
- FastChat - https://github.com/lm-sys/FastChat
- Multi-agent with Semantic Kernel - https://medium.com/@akshaykokane09/step-by-step-guide-to-develop-ai-multi-agent-system-using-microsoft-semantic-kernel-and-gpt-4o-f5991af40ea6
- Autogen examples - https://github.com/tylerprogramming/ai
- How to Use ANY Local Open-Source LLM with AutoGen in 5 MINUTES! - https://www.youtube.com/watch?v=OJFvBQQI9ME
- Multi-agent by Autogen in examples - https://microsoft.github.io/autogen/docs/Examples/
- Multi-Agent in examples by LangGraph - https://blog.langchain.dev/langgraph-multi-agent-workflows/
- AI example projects - https://github.com/insightbuilder/python_de_learners_data/tree/main/code_script_notebooks/projects
- How to create custom tool in LangChain - https://lancerninja.com/custom-tools-langchain/
- Building custom agent in LLamaIndex - https://docs.llamaindex.ai/en/latest/examples/agent/custom_agent/
- Semantic Kernel demos - https://github.com/qmatteoq/SemanticKernel-Demos
- ChatBot from scratch in TensorFlow - https://blog.tensorflow.org/2019/05/transformer-chatbot-tutorial-with-tensorflow-2.html
-
Mistral-finetune in use example - https://www.youtube.com/watch?v=fzT9BbHu3ec
Use of mistral-finetune, mistral-inference, how to format data
-
Fine-tune Mistral 7B using together.ai - https://youtu.be/Wqf2GimAlWo
- Role of Generative AI in Realm of Software Testing and Quality Assurance - https://www.ishir.com/blog/107077/generative-ai-in-software-testing-and-quality-assurance-domain.htm
- 10 AI Tools Every Software Developer Should Be Using in 2023 & Beyond: https://www.ishir.com/blog/90145/10-ai-tools-every-software-developer-should-be-using-in-2023-beyond.htm
- Image to text models - https://huggingface.co/models?pipeline_tag=image-to-text&sort=downloads
- Image to text mode with example - https://huggingface.co/nlpconnect/vit-gpt2-image-captioning