redshift006 Goto Github PK

aipnd-exercises

asynctaskpractice

blog

cpp

dj4e-projects

Communicate about projects related to the Django Courses

dj4e_solo_homework

django for everybody L32 Solo simple homework

geek_time_vim

Vim configuration for Geek Time

guide-to-mathematical-notation

Becoming "Wikipedia proficient"

ice

Imooc_ice

imooc_frontend_learing_code

intro-py-project-classify-pet-images

udacity-python AI intro, first project Learned more.

intro-to-self-driving-cars

Intro to Self-Driving Cars-Udacity Notes & Code

legacy-edge-launcher

Get back to Microsoft Edge Legacy as simple as possible!

linear-algebra-for-machine-learning-and-data-science

Notebooks and notes from going through Deeplearning.AI's linear algebra course

machine-learning-specialization-coursera

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

markmind-data

markmind data

python_practise

Geek time learning python course practise

qmsolve

⚛️ A module for solving and visualizing the Schrödinger equation.

sell_hungry

Imooc_vue_饿了么

shenghuanews

singlefile_articles_backup

SimpleRead_Articles_Backup

templatemsg

微信小程序下发模板消息

ud-isdc-selfdrivingcarsimulation

Udacity Intro to Self-Driving Car Nanodegree Project : Joy Ride (Simple Self-Driving Car Simulation)

udacity_cs101

Udacity_cs101_Code

udacity_introtoselfdrivingcarsnd

My solutions, projects and experiments of the Udacity Introduction to Self-Driving Cars Nanodegree (October 2017 - December 2017)

udacity_python_codes

Intro to Computer Science

udacitymatrixclass

Implementing a Matrix Class In this project you will implement a Matrix class in Python. Specifically, you will implement the following methods: class Matrix: def determinant(self): # your code def trace(self): # your code def inverse(self): # your code def transpose(self): # your code # Overloaded operators def __add__(self,other): # your code def __sub__(self,other): # your code def __mul__(self,other): # your code When your class is working properly you will be able to manipulate matrices in code as if they were regular numbers (for the most part). For example: > A = Matrix([ [2,4], [3,1] ]) > print( A.transpose()) 2.0 3.0 4.0 1.0 > > I = Matrix([ [1,0], [0,1] ]) > > print(A*I) 2.0 4.0 3.0 1.0 > > print(A * A.inverse()) 1.0 0.0 0.0 1.0 Project Instruction Your project workspace will contain several files. On the next page, you will see something like this: notebook screenshot We recommend that you open all of these files in new tabs (except datagenerator.py and test.py, which you won't need to modify) so that your browser looks something like this: jupyter multiple tabs matrix.py - This contains the beginnings of a Matrix class (which you will complete) as well as some helper functions zeroes and identity. This is the file you will be doing most of your work in. matrix_playground.ipynb - A notebook that imports your Matrix class and calls the test code. You may find it useful to use this notebook as a place to use the matrix math code you will write in matrix.py. matrix_cheat_sheet.ipynb - A Jupyter notebook with a glossary, explanation of matrix notation and list of matrix equations. Use this as a reference when filling out the methods in the Matrix class! kalman_filter_demo.ipynb - You don't need to do anything with this notebook but you may find it interesting. Once your matrix class is working properly, the KF implemented here will actually work! Other Files (feel free to ignore). test.py - Contains test code which demonstrates the expected functionality of your code. datagenerator.py - this just contains some helper code which is used by the Kalman Filter.

udcity_ai_python_intro_practise

Learning Code and some notes, thx Udacity!!!

videosplash

vue.d3.tree

Vue component to display tree based on D3.js layout.