imengby Goto Github PK

ainet2019-day-in-life-

amazon-kendra-langchain-extensions

Samples to build Generative AI applications with LangChain and Amazon Kendra

amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

aws-customer-churn-pipeline

An End to End Customer Churn Prediction solution using AWS services.

aws-dbs-refarch-graph

Reference Architectures for Graph Databases on AWS

aws-kendraretrievefalcon

bedrock-agent-txt2sql

Use natural language to Generate Amazon Athena SQL queries to fetch data.

classificationlstm

Classification LSTM RNN for DeepLog realization

deeplog

Pytorch implementation of DeepLog.

deeplog-1

ft-tree

gnn

TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

gnn-communication-networks

This is the repository for the collection of Graph-based Deep Learning for Communication Networks.

gnn4com

graph-notebook

Library extending Jupyter notebooks to integrate with Apache TinkerPop, openCypher, and RDF SPARQL.

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

ignnition

Framework for fast prototyping of Graph Neural Networks

loghub

A large collection of system log datasets for AI-powered log analytics

ml-ai-experiments

All my experiments with AI and ML

mlstarters

mod_llm

netsoft2019

Here you will find two jupyter notebook showing ARIMA and LSTM for timeseries Forecasting on real cloud data.

shortview-on-demoim2019

sla-ml

spell

Spell: Streaming parsing of system event logs

st-graphneuralnet

stanford-tensorflow-tutorials

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

tensorflow-without-a-phd

A crash course in six episodes for software developers who want to become machine learning practitioners.

vims-data