humasak's repositories

AI-Expert-Roadmap

Roadmap to becoming an Artificial Intelligence Expert in 2021

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DeepICF

TensorFlow Implementation of Deep Item-based Collaborative Filtering Model for Top-N Recommendation

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Bidirectional-LSTM-and-Convolutional-Neural-Network-For-Temperature-Prediction

We use weather data of Ulsan, Korea from 1980 to 2017 to predict temperature.

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CNN-RNN-Yield-Prediction

This repository contains codes for the paper entitled "A CNN-RNN Framework for Crop Yield Prediction"

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COBREXA.jl

Constraint-Based Reconstruction and EXascale Analysis

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crawler-google-places

Scrape information about places from Google Maps. Gives you extra information that you can't get using the Google Places API.

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datasharing

The Leek group guide to data sharing

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DeepFRI

Deep functional residue identification

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deepgoplus

DeepGO with GOPlus axioms

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examples

Grakn Example Projects and Tutorials

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feast

Feature Store for Machine Learning

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google-landmark

Dataset with 5 million images depicting human-made and natural landmarks spanning 200 thousand classes.

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grakn

Grakn Core: The Knowledge Graph

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graql

Graql: Grakn Query Language

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MLAI

Machine Learning And Adaptive Intelligence Module

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PlantVillage-Dataset

Dataset of diseased plant leaf images and corresponding labels

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ScalableML

COM6012 Scalable Machine Learning - University of Sheffield

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SinGAN-Tensorflow

Learning a generative model from a single natural image

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sql-to-graql

Introduction to Graql for SQLers

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STAR

RNA-seq aligner

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stylegan

StyleGAN - Official TensorFlow Implementation

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textbook

The textbook Computational and Inferential Thinking: The Foundations of Data Science

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Tourism-Place-Recommendation-System-Using-Hybrid-Filtering

Toba Tourism Place Hybrid Recommendation

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typedb-client-java

TypeDB Client for Java

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typedb-client-nodejs

TypeDB Client for Node.js

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typedb-client-python

TypeDB Client for Python

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typeql-lang-java

TypeQL language library for Java

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Yield-Prediction-DNN

This repository contains my code for the "Crop Yield Prediction Using Deep Neural Networks" paper.

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