Skip to main content

Installing Jupyter Notebook

Jupyter notebook allows you to work on different languages on a single paltform. Here We will see how to install jupyter using python commands. One of the advantage I have noticed in the notebook is that you don't have to keep all the required modules for your program in the system. You can just import it in the notebook itselg. For example, 'import numpy, will allow you to use the package directly from the notebook.

How to use IPython Notebook or Jupiter Notebook

First install ipython using the following command from shell :
pip install ipython
Next install Jupiter notebook as :
pip install jupyter
Open jupyter notebook from user (not from root) :
jupyter notebook
it showed some permission denied error like :
IOError: [Errno 13] Permission denied: '/run/user/6138/jupyter/notebook_cookie_secret'
I googled so much and the following fixed the code :
Run the command below
 export XDG_RUNTIME_DIR=""
Then again type
jupyter notebook
It will now open your default browser
And it will show all projects or works you have in system in the browser
If you want to run one new python file please click 'New' in the right side and from there select Python.
Here you can type your code and execute it either using 'shift'+'enter' or the Cells--> Run cells option
Now you can play with jupyter notebook !!!
Have a nice Day guys

Comments

Popular posts from this blog

Coursera Course 3 Structuring Machine Learning Projects

Week One - Video One - Why ML STrategy Why we should learn care about ML Strategy Here when we try to improve the performance of the system we should consider about a lot of things . They are: -Amount of data - Amount of diverse data - Train algorithm longer with gradient descent -use another optimization algorithm like Adam -  use bigger network or smaller network depending out requirement -  use drop out - add l2 regularization - network architecture parameters like number of hidden units, Activation function etc. Second Video - Orthogonalization Orthogonalization means in a deep learning network we can change/tune so many things for eg. hyper parameters to get a more performance in the network . So most effective people know what to tune in order to achieve a particular effect. For every set of problem there is a separate solution. Don't mix up the problems and solutions. For that, first we should find out where is the problem , whether it is with training ...

Libraries For ML Projects in Python

Top machine learning libraries for Python 1. Numpy Numerical Python It is the most fundamental package for scientific computing in python. It provides operations for matrix and array. Numpy arrays are used in most of the ML projects. The library provides vectorization of mathematical operations on the NumPy array type 2. Scipy modules for linear algebra, optimization, integration, and statistics. It contains modules for linear algebra, optimization, integration, and statistics. 3. Pandas It works with labelled and relational data.  It designed for quick and easy data manipulation, aggregation, and visualization. Here is just a small list of things that you can do with Pandas:     Easily delete and add columns from DataFrame     Convert data structures to DataFrame objects     Handle missing data, represents as NaNs     Powerful grouping by functionality 4. Matplotlib Used for  generation of simple and powerful visual...