Apache 2.0: Allows users to use the software for any purpose, to distribute it, to modify it, and to distribute modified versions of the software under the terms of the license without concern for royalties.
MIT: Similar to Apache 2.0 but shorter and more straightforward. Also, in contrast to Apache 2.0, it does not require stating any significant changes to the original code.
Disclaimer: The information provided in this repo does not, and is not intended to, constitute legal advice. Maintainers of this repo are not responsible for the actions of third parties who use the notebooks.
01_generate_text2image_sdxl.ipynb: generate an image with Stability AI's Stable Diffusion XL model and Amazon SageMaker JumpStart
An_introduction_to_explainable_AI_with_Shapley_values.ipynb: An introduction to explainable AI with Shapley values
Contextual_Chatbot_NLP_and_Tensorflow.ipynb: CHATBOTS - Using Natural Language Processing and Tensorflow
Copy_of_DLCourse.ipynb: Building Transformer Models with Attention Crash Course. Build a Neural Machine Translator in 12 Day
Copy_of_rag_fusion_pipeline.ipynb: This notebook shows how to implement RAG Fusion using the LlamaIndex Query Pipeline syntax
APACHEAGE_and_Neo4j.ipynb: I developed a notebook to show how to use Apache AGE and Neo4J in Google Colab. This notebook shows how to use LLMs with Neo4j, a graph database, to perform Retrieval Augmented Generation (RAG).
Cross_Entropy.ipynb: A Gentle Introduction to Cross-Entropy for Machine Learning
DBSWIM.ipynb: POC FOR FAA SWIM DATA WITH POSTGRESQL
DLCourse.ipynb: Building Transformer Models with Attention Crash Course. Build a Neural Machine Translator in 12 Day
Embedchain_Demo.ipynb: Embedchain is an open-source RAG Framework that makes creating and deploying AI apps easy. At its core, Embedchain follows the design principle of being "Conventional but Configurable" to serve both software and machine learning engineers. Here is a straightforward demo of how it works!
GPT4V_IMAGE_PATHPASSING.ipynb: image2text passing the imput as PATH
GPT4V_IMAGE_URLPASSING.ipynb: image2text passing the imput as URL
IMAGE_GENERATOR.ipynb: Generate Images with model DALLE-3 USING OPENAI API
LICENSE
LLAMA2.ipynb
LSTM_TIMESERIES.ipynb META_AI_SegmentAnythingModel(SAM)_predictor_example.ipynb
MISTRAL_TUTORIAL.ipynb MISTRAL_TUTORIAL_T4GPU.ipynb MISTRAL_TUTORIAL_a100gpuVt4gpu-A100.ipynb MISTRAL_TUTORIAL_a100gpuVt4gpu-A100GPU-2.ipynb
MambaGC.ipynb Migrate_from_pg_embedding_to_pg_vector.ipynb
Mistral-7B-Instruct-without-flash_attention_2.ipynb Mistral-7B-Instruct.ipynb Mistral-Embedchain_Demo.ipynb Mistral_Integration_with_Langchain_PostgreSQL.ipynb Mistral_in_AWS.ipynb
Mistral_in_AWS_with_TWOMODELS.ipynb Mistral_integration_with_langchain.ipynb
Mixtral_8x7B.ipynb: Flash Attention 2 with Prompt-Examples using model Mixtral_8x7B from huggingface
OpeanAIPOC.ipynb
PCA_COURSE2023.ipynb PCA_with_MNIST_Dataset.ipynb
PGEmbeddingEmbedding_T4.ipynb PGvectorEmbedding_CPU.ipynb
PatchTST.ipynb
RLHF_with_Custom_Datasets_EVAL5_A100.ipynb
Rag_Fusion_Pipeline_PostgreSQL_Embedding_Mistral.ipynb SemanticSearch.ipynb TransformerCourse.ipynb
aitutorials_custom_b747.ipynb
cv2023.ipynb: Computer Vision data.csv demo_supervision.ipynb
dogbreed.ipynb: Generate the identification of the dog's breed using model gpt-4-vision-preview based on OpenAI API.
first_order_model_demo.ipynb
gptvisionapi-final.ipynb gptvisionapi_complete.ipynb
langchain_opensourceLLM_mistral7B_openai.ipynb
mistral_rag_pgvector.ipynb mistral_test.ipynb mistral_test_AWS.ipynb
myknn.ipynb mysurya.ipynb
opeanai_aws_integration.ipynb: Integration OPENAI ANS AWS USING AWS LAMBDA FUNCTION
openai_pgvector_helloworld_FrankMorales_version.ipynb openai_pgvector_helloworld_FrankMorales_version_model_gpt-3.5-turbo-0613.ipynb openai_pgvector_helloworld_FrankMorales_version_model_gpt-4-0613.ipynb
part3_neural_network_mnist_data_with_rotations.ipynb
rag_fusion_pipeline_PostgreSQL_FM.ipynb
stable_diffusion.ipynb
time_series_exploratory_data_analysis_in_python.ipynb time_series_transformers.ipynb
transformer.ipynb transformer_TRANSLATOR.ipynb
transformer_from_scratch.ipynb: Create and train a transformer from scratch. Going through each foundational element step by step and explain what is happening along
transformermodel.ipynb