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anim_book_rec_app

Code for my book recommender application built with Flask

bertsum

Code for paper Fine-tune BERT for Extractive Summarization

cancer_risk_predictor

flask_nlp_app

NLP app built with Flask

flask_restful_api_lreg

building a RESTful API with Flask for a simple linear regression model

gensim

Topic Modelling for Humans

goodreads_book_rec_app

integration-tests-example

An example project showcasing how Ardan Labs writes integration tests

keras_dl_gans_faces

keras_neural_style_transfer

keras_practice_homl

keras_transfer_learning_practice

nlp_cleanlab_test

testing confident learning on sample text data to identify mislabelled categories

nlp_imdb_sentiment_analysis

nlp_practice_ak_feature_exploration

nlp_topic_modelling_practice_-news_articles-

prod_regmodel_flask_api

practice for productionising ML models as Flask RESTful API and setting up CI/CD pipeline

pt_bert_data_augmentation

pt_bert_multi_label_classification

fine-tuning BERT model on multi-label classification task

pt_bert_ner_practice

pyspark_recsys_als

ALS recommender system built with PySpark

python-testing-with-pytest

Excerpts from the book Python Testing with Pytest by Brian Okken

pytorch_binary_classifier

pytorch classifier built for the purposes of supervised sentiment analysis. this is achieved with a simple single-layer perceptron model and a multi-layer perceptron model, in addition to a simple word vectorizer

pytorch_finetune_glove_embeddings

transfer learning with GloVe embeddings on any corpus of text

rec_sys_collab_filter

record_price_predictor

A regression model for predicting the price of a record, trained using data from discogs.com

smote_practice

surprise_rec_sys

practice for building recommender systems with content-based algorithm and matrix factorization algorithms using surprise library

t5_finetuning_text_2_text

talknet2-pytorch

TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction.