python tutorial jupyter notebook

Notebook (IPyNB) instead of worrying about other things. You will not regret it. In short, IPyNB is a Python shell accessed through a browser. It allows you to mix and edit code, text, and graphics (or even interactive objects ). This article is completed in IPyNB. Almost all lectures in Python use IPython Notebook. I

Author: vamei Source: http://www.cnblogs.com/vamei welcome reprint, please also keep this statement. Thank you! Here are some tips I have learned when using python. These skills are frequently used when I use python. I recorded it in my notebook in a very fragmented manner. Now I will sort it out and share it with you. It also serves as a supplement to the

accessed through a browser. It allows you to mix and edit code, text, and graphics (even interactive objects). This article is done in the IPYNB. In the Python conference, almost all the speeches used Ipython Notebook. The IPYNB is preloaded in the anaconda and can be used directly. Here's what it looks like: In [1]: Print (' Hello World ') Hello World IPYNB is growing fast-I'm always shocked by the new

browser. It allows you to mix editing code, text, and graphics (even interactive objects). This article is done in the IPYNB. In the Python conference, almost all the speeches used Ipython notebook. The anaconda is preloaded with IPYNB and can be used directly. Here's what it looks like: In [1]: Print (' Hello World ') Hello World IPYNB is growing fast-I'm always amazed by the new features t

can see the virtual environment through pyenv Virtualenvs , I can see there is already a test.When I do not work in this directory, the Python version is the system default of 2.7.10. When I switched to the test directory, the Python version became the 3.4.2 I just set up, which is very handy.　　　　　　6. Additional expansion, using Jupyter:Jupyter is the product of stripping the IPython and

variance score = 0.34R2 score = 0.33Now we will create a 10-order polynomial and train the regression. and provide sample data points.polynomial = polynomialfeatures (degree = ten== [2.23, 1.35, 1.12== linear_ Model. Linearregression () Poly_linear_model.fit (x_train_transformed, y_train)print("\nlinear regression:\n", Reg_linear_mul.predict (datapoint))print("\ Npolynomial regression:\n", Poly_linear_model.predict (Poly_datapoint))Linear regression-[2.40170462]Polynomial regression-[1.8697225]

This tutorial is the first course of the cs231n course. English (written by Justin Johnson), please stamp here Chinese version (translated by Duke), please stamp here We will use the Python programming language to complete all the assignments in this course. Python is a great universal programming language, with the help of some common libraries (NumPy, scipy,