Advanced R - Online Book
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Data Science at the Command Line - Online Book
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Data Science at the Command Line - Online Book
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18 Impressive Applications of Generative Adversarial Networks (GANs)
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LSTM: How to Train Neural Networks to Write like Lovecraft
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LSTM: How to Train Neural Networks to Write like Lovecraft
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Indonesian App Review - Sentiment Analysis
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author: @andreas_chandra
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Medium
Indonesian App Review — Sentiment Analysis
Sentimen analisis adalah salah satu task dari text mining. Sentiment analisis juga menjadi projek yang paling utama bagi orang orang yang…
Why do Ride-Hailing App Drivers
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author: Ivan Sanders
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author: Ivan Sanders
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LinkedIn
Why do Ride-Hailing App Drivers Accept Your Order then Request You to Cancel it?
Originally published on Medium on 25th of March 2019. Maybe also check my other writings? If you are like me, an office worker in Jakarta in your 20s, chances are that you frequently use ride-hailing apps such as Grab or Gojek (Indonesian version of Uber/Grab)…
Bayesian Inference with Probabilistic Programming Using PyMC3
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author: Ali Akbar Septiandri
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author: Ali Akbar Septiandri
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Medium
Bayesian Inference with Probabilistic Programming Using PyMC3
How to do statistics as a computer scientist
Building Indonesian News Data set and Classifier
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author: @andreas_chandra
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Medium
Building Indonesian News Data set and Classifier
Berita yang kita baca sehari hari, berita yang tersebar dimana-mana baik yang gratis dan berbayar tak disangka merupakan data yang dapat…
GauGAN Turns Doodles into Stunning, Photorealistic Landscapes
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Analyze Jira Issues using BigQuery and Data Studio
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author: Rendy B. Junior
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author: Rendy B. Junior
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Will Google eventually switch from Tensorflow to Pytorch
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DLRM: An advanced, open source deep learning recommendation model
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Building Smarter AI Through Human-Machine Collaboration - Meetup
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DISKUSI MACHINE LEARNING -- PACMANN AI
LEARNING = REPRESENTATION + EVALUATION + OPTIMIZATION, Domingos (2012). Machine Learning bertujuan membuat fungsi yang dapat merepresentasikan data. Namun, representasi data saja tidaklah cukup untuk menyelesaikan masalah-masalah modeling. Supervisi yang tepat membuat model memfokuskan optimisasi sesuai dengan metrics dari masalah yang diselesaikan. Diskusi ini akan membahas secara umum pembentukan representasi dan optimisasi Machine Learning. Sebagai contoh, pembentukan model Metrics Learning dalam Recommender System dan face recognition. Mari datang dan belajar bersama-sama.
Diskusi ini gratis, tidak dipungut biaya.
Registrasi: bit.ly/all_ml_tasks
LEARNING = REPRESENTATION + EVALUATION + OPTIMIZATION, Domingos (2012). Machine Learning bertujuan membuat fungsi yang dapat merepresentasikan data. Namun, representasi data saja tidaklah cukup untuk menyelesaikan masalah-masalah modeling. Supervisi yang tepat membuat model memfokuskan optimisasi sesuai dengan metrics dari masalah yang diselesaikan. Diskusi ini akan membahas secara umum pembentukan representasi dan optimisasi Machine Learning. Sebagai contoh, pembentukan model Metrics Learning dalam Recommender System dan face recognition. Mari datang dan belajar bersama-sama.
Diskusi ini gratis, tidak dipungut biaya.
Registrasi: bit.ly/all_ml_tasks
New fast.ai course: A Code-First Introduction to Natural Language Processing
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A curated list of NLP papers - nlp-library
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ICML 2019 Videos
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A curated list of NLP papers - nlp-library
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ICML 2019 Videos
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Indonesian App Review — Sentiment Analysis using Neural Network and PyTorch
Author: @andreas_chandra
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Recommendation System Dengan Python : Definisi (Part 1)
Author: @zheaven07
http://bit.ly/2YOKMRN
Now, you can also submit your article to Data Folks Indonesia Publication on Medium. More information? poke the editor @andreas_chandra
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Author: @andreas_chandra
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Recommendation System Dengan Python : Definisi (Part 1)
Author: @zheaven07
http://bit.ly/2YOKMRN
Now, you can also submit your article to Data Folks Indonesia Publication on Medium. More information? poke the editor @andreas_chandra
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Facebook, Carnegie Mellon build first AI that beats pros in 6-player poker
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Multilingual Universal Sentence Encoder for Semantic Retrieval
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Advancing Semi-supervised Learning with Unsupervised Data Augmentation
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Multilingual Universal Sentence Encoder for Semantic Retrieval
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Advancing Semi-supervised Learning with Unsupervised Data Augmentation
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