DS Incubator (Summer 2020)
A collection of projects by the Made With ML Data Science Incubator cohort (Summer 2020) batch.
summer-2020 madewithml incubator
Projects
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Image Captioning
This project is an attempt to build image captioning models using CNN and Transformers. Libraries used - fastai2, Huggingface Tokenizers and WandB.
image-captioning representation-learning computer-vision arxiv:2006.06666
Ducky ML
React Native app with an intelligent journal and habit tracking. Uses Hugging Face to parse entries and conduct sentiment analysis. Developed using Expo.
conversational-ai sentiment-analysis visualization natural-language-processing
Movie Recommendation and Rating Prediction using KNN
A Movie Recommendation System and Rating Prediction using collaborative filtering by implementing the K-Nearest Neighbors algorithm.
machine-learning collaborative-filtering recommendation-system recommendation-systems
Toxic comment classification
This is a multi label classification project on toxic comment classification The dataset is from kaggle competition.
natural-language-processing classification code
Stack-GAN with BERT Embeddings
Text-to-image-synthesis of high quality flower images using stacked GAN architecture trained from scratch on 102-flowers dataset, with embeddings from ...
image-generation generative-adversarial-networks bert computer-vision
Recommendation systems
This recommendation system uses SVD for predicting the ratings.This also takes names of the cast , director and also keywords into consideration .
recommendation-systems scikit-learn python code
Text Classification- Genre prediction
Predicting the Genre of a movie based on the Movie description. Used the Logistic Regression model.
text-classification logistic-regression scikit-learn natural-language-processing
Movie Recommender App
This is an end-to-end movie recommender system. This project is unique in that it doesn't use any algorithm, and is built from scratch using Flask and ...
pandas flask data-science movielens
Movie recommender (3 simple strategies)
This recommender is comprised of 3 parts : Demographic Filtering, Content based filtering and Collaborative filtering
machine-learning recommendation-systems beginner code
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Authors
AI Research @apple. Author @oreillymedia. ML Lead @Ciitizen. Alum @hopkinsmedicine and @gatech
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