On this Github repo, navigate to the lab folder you want to run (lab1, lab2, lab3) and open the appropriate python notebook (*.ipynb). Click the "Run in Colab" link on the top of the lab. That's it!Running the labsNow, to run the labs, open the Jupyter notebook on Colab....
On this Github repo, navigate to the lab folder you want to run (lab1,lab2,lab3) and open the appropriate python notebook (*.ipynb). Click the "Run in Colab" link on the top of the lab. That's it! Running the labs Now, to run the labs, open the Jupyter notebook on Colab....
KellyHwong/MIT.Intro.To.CS.Pythongithub.com/KellyHwong/MIT.Intro.To.CS.Python Python入门的其他资料:KellyHwong/MIT.Intro.To.CS.PythonPython入门的其他资料: 笨方法学Python(适合新手)中英都有,百度 Python官方文档(可以看,也可以作为日常代码的资料库) https://docs.python.org/3/3.7.0 Documentation...
15-122 Principles of Imperative Computation 命令式编程C语言（需先修15-112的Python课）★ 15-150 P...
详情参见 GitHub 地址:https://github.com/aamini/introtodeeplearning 虽然门槛相对较低,但这门课程还是需要学生掌握最基本的线性代数和微积分知识,如矩阵相乘、求导、链式法则的运用等。Python 技能对上课有帮助,但并非必需。总体来看,该课程对新手非常友好,参加该课程的很多学生都来自非计算机科学领域。
首先，Python作为使用最广泛的编程语言，其在GitHub上的流行度高达18.17%，这反映出了Python语言的易用性及广泛的应用场景，从网站开发到数据科学，再到机器学习，Python无处不在。同时，这也展示了GitHub在支持多样编程语言和技术生态上的强大能力。此外，freeCodeCamp作为星标最多的项目，共有396万星，这不仅证明了...
dplyr frontend tutorial folder + intro tutorial + gitignore R project files 6年前 .travis.yml fix: set travis to use python 3.6 for f strings 5年前 ISSUE_TEMPLATE.md rm stackexchange 7年前 LICENSE Initial commit 10年前 Makefile.md
Intro to Machine Learning with PyTorch Nanodegree Program (Udacity) This nano degree program is prepared toteach you the foundational machine learning techniques, including data manipulation, supervised, and unsupervised algorithms. It is ideally prepared for students who have experience in Python and wan...
地址:https://open.163.com/newview/movie/courseintro?newurl=%2Fspecial%2Fopencourse%2Fcs50.html 6.0001: Introduction to Computer Science and Programming in Python 该课程适合很少或根本没有编程经验的学生。它旨在让学生了解计算在解决问题方面可以发挥的作用,并帮助所有专业的学生都有理由相信他们有能力编写...
Intro: A New way to Start Linear Algebra(简介:开始线性代数学习的新方法) Part 1: The Column Space of a Matrix(矩阵的列空间) Part 2: The Big Picture of Linear Algebra(线性代数概貌) Part 3: Orthogonal Vectors(正交向量) Part 4: Eigenvalues and Eigenvectors(特征值和特征向量) Part 5: Singula...