jameclear Goto Github PK

1806-spring17

18.06 course at MIT in Spring 2017

18303

18.303 - Linear PDEs course

18369

18.369/8.315 - Mathematical Methods in Nanophotonics course

2019fall_fa

2019秋季学期泛函分析笔记QAQ:unicorn:

6s083

Materials for MIT 6.S083 / 18.S190: Computational thinking with Julia + application to the COVID-19 pandemic

acousticbem

Boundary Element Method code for solving acoustic equations.

adda

ADDA - light scattering simulator based on the discrete dipole approximation

aero-matlab

My matlab homework files

ambar

:mag: Ambar: Document Search Engine

angler

Frequency-domain photonic simulation and inverse design optimization for linear and nonlinear devices

antenna-models

CST design files of antennas used in my researches and projects.

applyaf

Python module to apply antenna factors and cable losses to spectrum analyzer readings

automatic-udemy-course-enroller-get-paid-udemy-courses-for-free

Do you want to LEARN NEW STUFF for FREE? Don't worry, with the power of web-scraping and automation, this script will find the necessary Udemy coupons & enroll you for PAID UDEMY COURSES, ABSOLUTELY FREE!

awesome-git

A curated list of amazingly awesome Git tools, resources and shiny things

blitz-book

极速写书法

blitz-product

閃電式開發

blog

My Blog (Chinese)

bookdown

Authoring Books and Technical Documents with R Markdown

books

我读过的书。嘿嘿，分享给你。

bot5.club

Bot Friday Club - BOT5

brainultrasoundsimulation

Simulation of Ultrasound signals on a 3D Brain model using K-Wave toolbox. Plus simple ultrassound focusing algorithm.

ceviche

:shrimp: Electromagnetic Simulation + Automatic Differentiation

chebfun

Chebfun: numerical computing with functions.

chrono

C++ library for multi-physics simulation

classical-piano-composer

cvxpy

A Python-embedded modeling language for convex optimization problems.

data-analysis

Data Science Using Python

data-science-notes

数据科学的笔记以及资料搜集

deep-learning-for-metasurface-optimization

Deep Learning (non-linear regression) for metasurface parameter optimization using tensorflow/keras

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18个章节，50余万字。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[email protected] 版权所有，违权必究 Tan 2018.06