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Content-Based Recommender System recommends movies similar to the movie user likes and analyses the sentiments on the reviews given by the user for that movie.
With Amazon Rekognition Custom Labels, you can easily build and deploy Machine Learning (ML) models to identify custom objects which are specific to your business domain in images without requiring advanced ML knowledge. When combined with Amazon Augmented AI (A2I), you can quickly integrate a ML workflow to capture and label images with a human workforce for model training. As ML lifecycle is an iterative and repetitive process, you need to implement an effective workflow that can provide for continuous model training with new data and automated deployment. Your workflow also needs to be flexible enough to allow for changes without requiring development rework as your business objectives change. Operationalizing an effective and flexible workflow can be resource intensive, especially for customers who have limited machine learning capabilities. In this post, we will use AWS Step Functions, AWS Lambda, and AWS System Manager Parameter Store to automate a configurable ML workflow for Rekognition Custom Labels and A2I. We will provide an overview of the solution and instructions to deploy it with AWS CloudFormation.
A demo to test Custom Labels with models trained by Amazon Rekognition
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Build a Custom Object Detection Model from Scratch with Amazon SageMaker and Deploy it at the Edge with AWS DeepLens. This workshop explains how you can leverage DeepLens to capture data at the edge and build a training data set with Amazon SageMaker Ground Truth. Then, train an object detection model with Amazon SageMaker and deploy it to AWS DeepLens.
Amazon Textract Code Samples
Reading list for research topics in multimodal machine learning
AWS tutorial code.
code for 2019 bytedance icme
Fully automated end-to-end framework to extract data from bar plots and other figures in scientific research papers using modules such as OpenCV, AWS-Rekognition.
CMU MultimodalSDK is a machine learning platform for development of advanced multimodal models as well as easily accessing and processing multimodal datasets.
Python
A Comparative Framework for Multimodal Recommender Systems
A recommender system that enables cross-sell and upsell of products (either new products or already bought products) that will enable higher revenue generation. The data captures material that is sent to the wholesalers over a span of time.
Implementation of Youtube Recommendations Using Deep Learning
DeepFashion2 Dataset https://arxiv.org/pdf/1901.07973.pdf
EDUVSUM is a multimodal neural architecture that utilizes state-of-the-art audio, visual and textual features to identify important temporal segments in educational videos.
Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code!
The Code for ICME2019 Grand Challenge: Short Video Understanding (Single Model Ranks 6th)
icme2019_challenge-dataset
Copy-move forgery detection on digital image using Python
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
Config files for my GitHub profile.
MMGCN: Multi-modal Graph Convolution Network forPersonalized Recommendation of Micro-video
Code for the paper "Multimodal Review Generation for Recommender Systems", WWW'19
code for Image Manipulation Detection by Multi-View Multi-Scale Supervision
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.