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veronikakurth / repository
This sample shows how to create a Jupyter Notebook with the interactive pivot table and pivot charts components. This approach can be used for data analysis and data visualization purposes.
This sample shows how to create a Jupyter Notebook with the interactive pivot table and pivot charts components. This approach can be used for data analysis and data visualization purposes.
The data used in this sample can be downloaded from Kaggle.
Selected from shared topics, language and repository description—not editorial ratings.
botprof /
This Jupyter notebook shows one way to implement a simple first-order low-pass filter on sampled data in discrete time.
AliakbarETH /
Plotting a histogram of iris data For the exercises in this section, you will use a classic data set collected by botanist Edward Anderson and made famous by Ronald Fisher, one of the most prolific statisticians in history. Anderson carefully measured the anatomical properties of samples of three different species of iris, Iris setosa, Iris versicolor, and Iris virginica. The full data set is available as part of scikit-learn. Here, you will work with his measurements of petal length. Plot a histogram of the petal lengths of his 50 samples of Iris versicolor using matplotlib/seaborn's default settings. Recall that to specify the default seaborn style, you can use sns.set(), where sns is the alias that seaborn is imported as. The subset of the data set containing the Iris versicolor petal lengths in units of centimeters (cm) is stored in the NumPy array versicolor_petal_length. In the video, Justin plotted the histograms by using the pandas library and indexing the DataFrame to extract the desired column. Here, however, you only need to use the provided NumPy array. Also, Justin assigned his plotting statements (except for plt.show()) to the dummy variable _. This is to prevent unnecessary output from being displayed. It is not required for your solutions to these exercises, however it is good practice to use it. Alternatively, if you are working in an interactive environment such as a Jupyter notebook, you could use a ; after your plotting statements to achieve the same effect. Justin prefers using _. Therefore, you will see it used in the solution code.
lilyprojectwork /
(1) Use this walkthrough to setup Python with Keras in Google Drive: https://medium.com/deep-learning-turkey/google-colab-free-gpu-tutorial-e113627b9f5d (You don't have to read the whole thing---you might find it helpful/interesting to do so, but for now you only need to get up to the point where tensorflow 2.0 and keras are installed and imported.) (2) Put the attached two Jupyter notebook files in your Google drive colab folder and read through them line-by-line and try to understand what each command does. (There's no comments, so you might have to look up some of the commands, but most of them are self-explanatory so I think you'll be able to figure it out just by looking at the code.) If you're stuck/confused by any parts of it, ask me by email (I'll also quickly walk through it next week at the beginning of class). (3) Play around with the hyperparameters and neural net architecture (feel free to be creative and experiment and try different things even if you don't know how well they'll do!) and see what is the best validation accuracy you can acheive for a MLP network (fully connected layers of hidden neurons, the kind we've been studying before) and for a CNN network (the convolution kind we just started studying). HINT: you don't need to use all the code in these files for this, only the part up to the "history" where the model is trained and then the following command that computes the validation accuracy (all the code after that is just to randomly sample an image in the database and see how the model labels it). You don't have to turn anything in here, but we'll start class next week with a fun informal competition to see which student in the class got the highest accuracies for the two types (MLP and CNN) and then have the student(s) either describe their choices or share their screen and show the rest of us.
danielpm1982 /
This is a Jupyter Notebook that shows the basics about how to use Pandas lib along with Anaconda managed environments
This Jupyter Notebook demonstrates Random Sample Imputation for numerical missing data. Using a Titanic dataset, it shows how to replace missing values by sampling from existing non-missing data, and then analyzes the impact on feature distributions and variance.
danielpm1982 /
This is a Jupyter Notebook that shows the basics about how to use Jupyter Notebooks along with Anaconda managed environments