Ancalasenja (ancalasenja)

ancalasenja

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Location:Tokyo

Twitter:@ancalasenja

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Ancalasenja's starred repositories

CrackingMachineLearningInterview

A repository to prepare you for your machine learning interview, involving most of the questions asked by all the tech giants and local companies. Do this to Ace your Machine Learning Engineer Interviews

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awesome-papers-fewshot

Collection for Few-shot Learning

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flutter

Flutter makes it easy and fast to build beautiful apps for mobile and beyond

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flutter-ui-kits

Free Flutter UI Kits developed by Olayemi Garuba

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Best-Flutter-UI-Templates

completely free for everyone. Its build-in Flutter Dart.

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Awesome-Flutter-Templates

A Huge Collection of Flutter App templates

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PracticalDataScience-ENCA

The goal of this project is to recommend songs for a given playlist from Spotify. This project starts from data collection all the way to model deployment to ensure you have a working model to showcase.

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spotify-recommendation-system

Spotify recommendation system that recommends new songs for your playlist

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Twitter-Sentiment-Analysis

It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization

Language:Jupyter NotebookLicense:GPL-3.0Stargazers:220Issues:0Issues:0

data-science-portfolio

Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

Language:Jupyter NotebookLicense:MITStargazers:1072Issues:0Issues:0

Dataset-Sentimen-Analisis-Bahasa-Indonesia

Repositori ini merupakan kumpulan dataset terkait analisis sentimen Berbahasa Indonesia. Apabila Anda menggunakan dataset-dataset yang ada pada repositori ini untuk penelitian, maka cantumkanlah/kutiplah jurnal artikel terkait dataset tersebut. Dataset yang tersedia telah diimplementasikan dalam beberapa penelitian dan hasilnya telah dipublikasikan pada artikel/paper ilmiah.

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indolem

IndoLEM is a comprehensive Indonesian NLU benchmark, comprising three pillars NLP task: morpho-syntax, semantic, and discourse. Presented in COLING 2020.

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NLP_bahasa_resources

A Curated List of Dataset and Usable Library Resources for NLP in Bahasa Indonesia

License:MITStargazers:469Issues:0Issues:0

awesome-dataviz

:chart_with_upwards_trend: A curated list of awesome data visualization libraries and resources.

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awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

License:MITStargazers:24401Issues:0Issues:0

500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

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awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

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awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

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awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

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pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

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pytorch-cifar100

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

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EfficientNet-PyTorch

A PyTorch implementation of EfficientNet

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PyTorch-GAN

PyTorch implementations of Generative Adversarial Networks.

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ganhacks

starter from "How to Train a GAN?" at NIPS2016

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CycleGAN

Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.

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AdversarialNetsPapers

Awesome paper list with code about generative adversarial nets

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SpectralNormalizationKeras

Spectral Normalization for Keras Dense and Convolution Layers

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awesome-public-datasets

A topic-centric list of HQ open datasets.

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deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

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