GGYIMAH1031

Data Scientist / Deep Learning Engineer

Missouri University of Science & TechnologyMissouri, USA

Pinned Repositories

2016_POTUS_Election-Sentiment_Analysis_And_Topic_Modeling
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Building_A_Spam_Filter
Building A Spam Filter Using the Naive Bayes Classifier
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Conjoint_Analysis_Mobile_Service_Preferences
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Image_Data_for_Formation_Recognition_-_Obstacle_Detection
This repository contains train and test data sets for building a convolutional neural network model, which is able to recognize different soil/rock formations as well as detect obstacles in a mining / construction environment. This effort is towards the development of smart, autonomous excavators which are able to recognize different excavating environments and adjust the digging strategy accordingly.
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jupyter
Jupyter metapackage for installation, docs and chat
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Market_Analysis-Predicting_Customer_Churn
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MarketBasketAnalysis
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Predicting-Tennis-Matches-Live-Betting-
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Predictive_Maintenance_Analytics
A model for predicting imminent machine failure
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Smart_Surveillance
An intelligent video surveillance system that flags suspicious activities.
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GGYIMAH1031's Repositories

GGYIMAH1031/Adminator-admin-dashboard
Adminator is a easy to use and well design admin dashboard template for web apps, websites, services and more
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GGYIMAH1031/autokeras
accessible AutoML for deep learning.
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GGYIMAH1031/awesome-data-engineering
A curated list of data engineering tools for software developers
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GGYIMAH1031/Better-Python-59-Ways
Code Sample of Book "Effective Python: 59 Specific Ways to Write Better Pyton" by Brett Slatkin
Language:Python
GGYIMAH1031/Cookbook
The Data Engineering Cookbook
GGYIMAH1031/d2l-en
Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook. With code, math, and discussions.
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GGYIMAH1031/data-engineering-gcp
Data Engineering on Google Cloud Platform
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GGYIMAH1031/Data-Engineering-HowTo
A list of useful resources to learn Data Engineering from scratch
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GGYIMAH1031/Data-Engineering-on-GCP-Cheatsheet
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GGYIMAH1031/DeepLearningForTimeSeriesForecasting
A tutorial demonstrating how to implement deep learning models for time series forecasting
GGYIMAH1031/featuretools
An open source python framework for automated feature engineering
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GGYIMAH1031/grokking-system-design
Thanks to lirenTu@scale
GGYIMAH1031/h2o-3
Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
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GGYIMAH1031/incubator-mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
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GGYIMAH1031/lingvo
Lingvo
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GGYIMAH1031/machine-learning-systems-design
A booklet on machine learning systems design with exercises
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GGYIMAH1031/ODM
A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images.
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GGYIMAH1031/OpenCV-3-Computer-Vision-with-Python-Cookbook
Published by Packt
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GGYIMAH1031/practicalAI
📚 A practical approach to machine learning.
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GGYIMAH1031/public-apis
A collective list of free APIs for use in software and web development.
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GGYIMAH1031/pyntcloud
pyntcloud is a Python library for working with 3D point clouds.
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GGYIMAH1031/PySimpleGUI
Launched in 2018 Actively developed and supported. Supports tkinter, Qt, WxPython, Remi (in browser). Create custom layout GUI's simply. Python 2.7 & 3 Support. 100+ Demo programs & Cookbook for rapid start. Extensive documentation. Examples using Machine Learning(GUI, OpenCV Integration, Chatterbot), Floating Desktop Widgets, Matplotlib + Pyplot integration, add GUI to command line scripts, PDF & Image Viewer. For both beginning and advanced programmers .
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GGYIMAH1031/refresher
Repo for refresher courses
GGYIMAH1031/segmentation_models
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
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GGYIMAH1031/seldon-core
Machine Learning Deployment for Kubernetes
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GGYIMAH1031/someMostWantedBooks
GGYIMAH1031/spark-deep-learning
Deep Learning Pipelines for Apache Spark
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GGYIMAH1031/stat-cookbook
:orange_book: The probability and statistics cookbook
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GGYIMAH1031/TensorFlowOnSpark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.
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GGYIMAH1031/yapf
A formatter for Python files
Language:Python