laizacavalcante
Enthusiastic learner of Data Science, GIS, and Remote Sensing. I spend most of my time playing with data, satellite images, and GIS stuff.
São Paulo, Brazil
laizacavalcante's Stars
anuraghazra/github-readme-stats
:zap: Dynamically generated stats for your github readmes
donnemartin/data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
EthicalML/awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
alexeygrigorev/data-science-interviews
Data science interview questions and answers
satellite-image-deep-learning/techniques
Techniques for deep learning with satellite & aerial imagery
ipython-contrib/jupyter_contrib_nbextensions
A collection of various notebook extensions for Jupyter
zedr/clean-code-python
:bathtub: Clean Code concepts adapted for Python
Naereen/badges
:pencil: Markdown code for lots of small badges :ribbon: :pushpin: (shields.io, forthebadge.com etc) :sunglasses:. Contributions are welcome! Please add yours!
gee-community/geemap
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
ashishpatel26/Andrew-NG-Notes
This is Andrew NG Coursera Handwritten Notes.
githubharald/SimpleHTR
Handwritten Text Recognition (HTR) system implemented with TensorFlow.
giswqs/earthengine-py-notebooks
A collection of 360+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping
svsool/memo
Markdown knowledge base with bidirectional [[link]]s built on top of VSCode
ashishpatel26/Amazing-Feature-Engineering
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
renelikestacos/Google-Earth-Engine-Python-Examples
Various examples for Google Earth Engine in Python using Jupyter Notebook
csaybar/EEwPython
A series of Jupyter notebook to learn Google Earth Engine with Python
ajwdewit/pcse
Repository for the Python Crop Simulation Environment
samsammurphy/gee-atmcorr-S2
Atmospheric correction of a (single) Sentinel 2 image
pysal/tobler
Areal interpolation, Dasymetric Mapping, & Change of Support
stevenpawley/Pyspatialml
Machine learning modelling for spatial data
r-spatial/dtwSat
Time-Weighted Dynamic Time Warping for satellite image time series analysis
ermongroup/tile2vec
Implementation and examples for Tile2Vec
IPL-UV/ee_ipl_uv
Multitemporal Cloud Masking in the Google Earth Engine
CosmiQ/CometTS
Comet Time Series Toolset for working with a time-series of remote sensing imagery and user defined polygons
Harvard-IACS/2019-CS109B
awangenh/Weed-Mapping
Weed Mapping in Aerial Images through Identification and Segmentation of Crop Rows and Weeds using Convolutional Neural Networks
brazil-data-cube/stmetrics
Time Series Metrics
harry-gibson/modis-gapfilling-full
andersonreisoares/dtaidistance
Time series distances: Dynamic Time Warping (DTW)