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expert_readed_books

2021年最新总结，推荐工程师合适读本，计算机科学，软件技术，创业，**类，数学类，人物传记书籍

fashion-mnist

A MNIST-like fashion product database. Benchmark :point_right:

go_rl

Designing Graphene Oxide with High Toughness Using Deep Reinforcement Learning

greenpepper

hand-gesture-recognition

CNN-based transfer learning with AlexNet :v:

handrecognition

Machine Learning example project using images of hand gestures.

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

hello-quantum-world.

We live in the future 🤯

hello-world

The first program in github

hotbit

ASE density-functional tight-binding calculator

intro-to-materials-informatics

Educational materials (slides, outlines, drafts, code) for single-lecture introduction to materials informatics

introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning

jisa

Standardised instrument control, data handling and simple GUI creation library for making experiment control programs.

julia-mit

Tutorials and information on the Julia language for MIT numerical-computation courses.

kalman_filters

Kalman filter finds the most optimum averaging factor for each consequent state. Also somehow remembers a little bit about the past states.

learn-pandas

Tutorials on how to use pandas effectively to do data analysis

learn_python

machine-learning-with-python

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

markov-introverts

Two-state Markov chain model of introversion (i.e. fun way to learn some Julia)

matlab-keithley2450

Matlab class for controlling a Keithley 2450 sourcemeter

mece4520

Materials for the course: Data Science for Mechanical System

melm

mit-linear-algebra-notes

Notes for MIT-Linear-Algebra

mitx-6.86x-machine-learning

Python code written for MIT's Machine Learning course offered on edX

mitx_6.86x

MITx 6.86x - Machine Learning with Python: from Linear Models to Deep Learning

ml-tutorial

Introduction to ML packages for the 6.86x course

ml_for_kirigami_design

Python package to model and to perform topology optimization for graphene kirigami using deep learning

ne_3d_cnn

neural-signal-analysis

nlp_twitter_sentiment_analysis

NLP Twitter Sentiment Analysis (Machine Learning)