Chuang's repositories
06_Python_Object_Class
Object-oriented programming (OOP) is a method of structuring a program by bundling related properties and behaviors into individual objects. In this tutorial, you’ll learn the basics of object-oriented programming in Python.
abTEM
ab initio Transmission Electron Microscopy
BayesianOptimization
A Python implementation of global optimization with gaussian processes.
DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为15个章节，近20万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
electron_diffraction_simulation
Electron Diffraction Simulation
exercise
exercise for nndl
Hyperspy_Workshop_2021
This repository contains the notebooks used for the 2021 Hyperspy Workshop at ePSIC, Diamond Light Source.
MathStatsCode
Codes for my mathematical statistics course
MSE672-Introduction-to-TEM
MSE672 Course of the MSE Department at UTK in the Spring Semester 2021
nndl-exercise-ans
Solutions for nndl/exercise
notes-python
中文 Python 笔记
Numfys_rus
Лабораторные работы по физике на языке Python
PML_Workshops
The WashU Physics Machine Learning Club puts jupyter notebooks we will be using internally in this repo. We do not claim authorship of the notebooks.
Ptycho_STEM
Codes for recontrusting phase image of materials based on ptychography
PtyLab.py
Python implementation of ptylab
Python
All Algorithms implemented in Python
Python-for-Probability-Statistics-and-Machine-Learning
Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
RCworkshops
ASU Research Computing :: materials from hosted workshops
Revealing-Ferroelectric-Switching-Character-Using-Deep-Recurrent-Neural-Networks
The ability to manipulate domains and domain walls underpins function in a range of next-generation applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of features of nanoscale ferroelectric switching from multichannel hyperspectral band-excitation piezoresponse force microscopy of tensile-strained PbZr0.2Ti0.8O3 with a hierarchical domain structure. Using this approach, we identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we are able to identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of the physical response of a material from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging multimodal in operando spectroscopies and automated control for the manipulation of nanoscale structures in materials.
Self-Learning
Books Papers, Courses & more I have to learn soon
stem-learning
Package to use an FCN to learn defects from STEM imaging
stemtool
A single package for analyzing atomic resolution STEM, 4D-STEM and STEM-EELS datasets, along with basic STEM simulation functionality
tensorflow2_tutorials_chinese
tensorflow2中文教程，持续更新(当前版本:tensorflow2.0)，tag: tensorflow 2.0 tutorials