data-science-ipython-notebooks

Index

• deep-learning
  • tensorflow
  • theano
  • keras
  • caffe
• scikit-learn
• statistical-inference-scipy
• pandas
• matplotlib
• numpy
• python-data
• kaggle-and-business-analyses
• spark
• mapreduce-python
• amazon web services
• command lines
• misc
• notebook-installation
• credits
• contributing
• contact-info
• license

deep-learning

IPython Notebook(s) demonstrating deep learning functionality.

tensor-flow-tutorials

Additional TensorFlow tutorials:

• pkmital/tensorflow_tutorials
• nlintz/TensorFlow-Tutorials
• alrojo/tensorflow-tutorial
• BinRoot/TensorFlow-Book

Notebook	Description
tsf-basics	Learn basic operations in TensorFlow, a library for various kinds of perceptual and language understanding tasks from Google.
tsf-linear	Implement linear regression in TensorFlow.
tsf-logistic	Implement logistic regression in TensorFlow.
tsf-nn	Implement nearest neighboars in TensorFlow.
tsf-alex	Implement AlexNet in TensorFlow.
tsf-cnn	Implement convolutional neural networks in TensorFlow.
tsf-mlp	Implement multilayer perceptrons in TensorFlow.
tsf-rnn	Implement recurrent neural networks in TensorFlow.
tsf-gpu	Learn about basic multi-GPU computation in TensorFlow.
tsf-gviz	Learn about graph visualization in TensorFlow.
tsf-lviz	Learn about loss visualization in TensorFlow.

tensor-flow-exercises

Notebook	Description
tsf-not-mnist	Learn simple data curation by creating a pickle with formatted datasets for training, development and testing in TensorFlow.
tsf-fully-connected	Progressively train deeper and more accurate models using logistic regression and neural networks in TensorFlow.
tsf-regularization	Explore regularization techniques by training fully connected networks to classify notMNIST characters in TensorFlow.
tsf-convolutions	Create convolutional neural networks in TensorFlow.
tsf-word2vec	Train a skip-gram model over Text8 data in TensorFlow.
tsf-lstm	Train a LSTM character model over Text8 data in TensorFlow.

theano-tutorials

Notebook	Description
theano-intro	Intro to Theano, which allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.
theano-scan	Learn scans, a mechanism to perform loops in a Theano graph.
theano-logistic	Implement logistic regression in Theano.
theano-rnn	Implement recurrent neural networks in Theano.
theano-mlp	Implement multilayer perceptrons in Theano.

keras-tutorials

Notebook	Description
keras	Keras is an open source neural network library written in Python. It is capable of running on top of either Tensorflow or Theano.
setup	Learn about the tutorial goals and how to set up your Keras environment.
intro-deep-learning-ann	Get an intro to deep learning with Keras and Artificial Neural Networks (ANN).
theano	Learn about Theano by working with weights matrices and gradients.
keras-otto	Learn about Keras by looking at the Kaggle Otto challenge.
ann-mnist	Review a simple implementation of ANN for MNIST using Keras.
conv-nets	Learn about Convolutional Neural Networks (CNNs) with Keras.
conv-net-1	Recognize handwritten digits from MNIST using Keras - Part 1.
conv-net-2	Recognize handwritten digits from MNIST using Keras - Part 2.
keras-models	Use pre-trained models such as VGG16, VGG19, ResNet50, and Inception v3 with Keras.
auto-encoders	Learn about Autoencoders with Keras.
rnn-lstm	Learn about Recurrent Neural Networks (RNNs) with Keras.
lstm-sentence-gen	Learn about RNNs using Long Short Term Memory (LSTM) networks with Keras.

deep-learning-misc

Notebook	Description
deep-dream	Caffe-based computer vision program which uses a convolutional neural network to find and enhance patterns in images.

scikit-learn

IPython Notebook(s) demonstrating scikit-learn functionality.

Notebook	Description
intro	Intro notebook to scikit-learn. Scikit-learn adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays.
knn	Implement k-nearest neighbors in scikit-learn.
linear-reg	Implement linear regression in scikit-learn.
svm	Implement support vector machine classifiers with and without kernels in scikit-learn.
random-forest	Implement random forest classifiers and regressors in scikit-learn.
k-means	Implement k-means clustering in scikit-learn.
pca	Implement principal component analysis in scikit-learn.
gmm	Implement Gaussian mixture models in scikit-learn.
validation	Implement validation and model selection in scikit-learn.

statistical-inference-scipy

IPython Notebook(s) demonstrating statistical inference with SciPy functionality.

Notebook	Description
scipy	SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data.
effect-size	Explore statistics that quantify effect size by analyzing the difference in height between men and women. Uses data from the Behavioral Risk Factor Surveillance System (BRFSS) to estimate the mean and standard deviation of height for adult women and men in the United States.
sampling	Explore random sampling by analyzing the average weight of men and women in the United States using BRFSS data.
hypothesis	Explore hypothesis testing by analyzing the difference of first-born babies compared with others.

pandas

IPython Notebook(s) demonstrating pandas functionality.

Notebook	Description
pandas	Software library written for data manipulation and analysis in Python. Offers data structures and operations for manipulating numerical tables and time series.
github-data-wrangling	Learn how to load, clean, merge, and feature engineer by analyzing GitHub data from the Viz repo.
Introduction-to-Pandas	Introduction to Pandas.
Introducing-Pandas-Objects	Learn about Pandas objects.
Data Indexing and Selection	Learn about data indexing and selection in Pandas.
Operations-in-Pandas	Learn about operating on data in Pandas.
Missing-Values	Learn about handling missing data in Pandas.
Hierarchical-Indexing	Learn about hierarchical indexing in Pandas.
Concat-And-Append	Learn about combining datasets: concat and append in Pandas.
Merge-and-Join	Learn about combining datasets: merge and join in Pandas.
Aggregation-and-Grouping	Learn about aggregation and grouping in Pandas.
Pivot-Tables	Learn about pivot tables in Pandas.
Working-With-Strings	Learn about vectorized string operations in Pandas.
Working-with-Time-Series	Learn about working with time series in pandas.
Performance-Eval-and-Query	Learn about high-performance Pandas: eval() and query() in Pandas.

matplotlib

IPython Notebook(s) demonstrating matplotlib functionality.

Notebook	Description
matplotlib	Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
matplotlib-applied	Apply matplotlib visualizations to Kaggle competitions for exploratory data analysis. Learn how to create bar plots, histograms, subplot2grid, normalized plots, scatter plots, subplots, and kernel density estimation plots.
Introduction-To-Matplotlib	Introduction to Matplotlib.
Simple-Line-Plots	Learn about simple line plots in Matplotlib.
Simple-Scatter-Plots	Learn about simple scatter plots in Matplotlib.
Errorbars.ipynb	Learn about visualizing errors in Matplotlib.
Density-and-Contour-Plots	Learn about density and contour plots in Matplotlib.
Histograms-and-Binnings	Learn about histograms, binnings, and density in Matplotlib.
Customizing-Legends	Learn about customizing plot legends in Matplotlib.
Customizing-Colorbars	Learn about customizing colorbars in Matplotlib.
Multiple-Subplots	Learn about multiple subplots in Matplotlib.
Text-and-Annotation	Learn about text and annotation in Matplotlib.
Customizing-Ticks	Learn about customizing ticks in Matplotlib.
Settings-and-Stylesheets	Learn about customizing Matplotlib: configurations and stylesheets.
Three-Dimensional-Plotting	Learn about three-dimensional plotting in Matplotlib.
Geographic-Data-With-Basemap	Learn about geographic data with basemap in Matplotlib.
Visualization-With-Seaborn	Learn about visualization with Seaborn.

numpy

IPython Notebook(s) demonstrating NumPy functionality.

Notebook	Description
numpy	Adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays.
Introduction-to-NumPy	Introduction to NumPy.
Understanding-Data-Types	Learn about data types in Python.
The-Basics-Of-NumPy-Arrays	Learn about the basics of NumPy arrays.
Computation-on-arrays-ufuncs	Learn about computations on NumPy arrays: universal functions.
Computation-on-arrays-aggregates	Learn about aggregations: min, max, and everything in between in NumPy.
Computation-on-arrays-broadcasting	Learn about computation on arrays: broadcasting in NumPy.
Boolean-Arrays-and-Masks	Learn about comparisons, masks, and boolean logic in NumPy.
Fancy-Indexing	Learn about fancy indexing in NumPy.
Sorting	Learn about sorting arrays in NumPy.
Structured-Data-NumPy	Learn about structured data: NumPy's structured arrays.

python-data

IPython Notebook(s) demonstrating Python functionality geared towards data analysis.

Notebook	Description
data structures	Learn Python basics with tuples, lists, dicts, sets.
data structure utilities	Learn Python operations such as slice, range, xrange, bisect, sort, sorted, reversed, enumerate, zip, list comprehensions.
functions	Learn about more advanced Python features: Functions as objects, lambda functions, closures, *args, **kwargs currying, generators, generator expressions, itertools.
datetime	Learn how to work with Python dates and times: datetime, strftime, strptime, timedelta.
logging	Learn about Python logging with RotatingFileHandler and TimedRotatingFileHandler.
pdb	Learn how to debug in Python with the interactive source code debugger.
unit tests	Learn how to test in Python with Nose unit tests.

kaggle-and-business-analyses

IPython Notebook(s) used in kaggle competitions and business analyses.

Notebook	Description
titanic	Predict survival on the Titanic. Learn data cleaning, exploratory data analysis, and machine learning.
churn-analysis	Predict customer churn. Exercise logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors. Includes discussions of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.

spark

IPython Notebook(s) demonstrating spark and HDFS functionality.

Notebook	Description
spark	In-memory cluster computing framework, up to 100 times faster for certain applications and is well suited for machine learning algorithms.
hdfs	Reliably stores very large files across machines in a large cluster.

mapreduce-python

IPython Notebook(s) demonstrating Hadoop MapReduce with mrjob functionality.

Notebook	Description
mapreduce-python	Runs MapReduce jobs in Python, executing jobs locally or on Hadoop clusters. Demonstrates Hadoop Streaming in Python code with unit test and mrjob config file to analyze Amazon S3 bucket logs on Elastic MapReduce. Disco is another python-based alternative.

aws

IPython Notebook(s) demonstrating Amazon Web Services (AWS) and AWS tools functionality.

Also check out:

• SAWS: A Supercharged AWS command line interface (CLI).
• Awesome AWS: A curated list of libraries, open source repos, guides, blogs, and other resources.

Notebook	Description
boto	Official AWS SDK for Python.
s3cmd	Interacts with S3 through the command line.
s3distcp	Combines smaller files and aggregates them together by taking in a pattern and target file. S3DistCp can also be used to transfer large volumes of data from S3 to your Hadoop cluster.
s3-parallel-put	Uploads multiple files to S3 in parallel.
redshift	Acts as a fast data warehouse built on top of technology from massive parallel processing (MPP).
kinesis	Streams data in real time with the ability to process thousands of data streams per second.
lambda	Runs code in response to events, automatically managing compute resources.

commands

IPython Notebook(s) demonstrating various command lines for Linux, Git, etc.

Notebook	Description
linux	Unix-like and mostly POSIX-compliant computer operating system. Disk usage, splitting files, grep, sed, curl, viewing running processes, terminal syntax highlighting, and Vim.
anaconda	Distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment.
ipython notebook	Web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document.
git	Distributed revision control system with an emphasis on speed, data integrity, and support for distributed, non-linear workflows.
ruby	Used to interact with the AWS command line and for Jekyll, a blog framework that can be hosted on GitHub Pages.
jekyll	Simple, blog-aware, static site generator for personal, project, or organization sites. Renders Markdown or Textile and Liquid templates, and produces a complete, static website ready to be served by Apache HTTP Server, Nginx or another web server.
pelican	Python-based alternative to Jekyll.
django	High-level Python Web framework that encourages rapid development and clean, pragmatic design. It can be useful to share reports/analyses and for blogging. Lighter-weight alternatives include Pyramid, Flask, Tornado, and Bottle.

misc

IPython Notebook(s) demonstrating miscellaneous functionality.

Notebook	Description
regex	Regular expression cheat sheet useful in data wrangling.
algorithmia	Algorithmia is a marketplace for algorithms. This notebook showcases 4 different algorithms: Face Detection, Content Summarizer, Latent Dirichlet Allocation and Optical Character Recognition.

notebook-installation

anaconda

Anaconda is a free distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing that aims to simplify package management and deployment.

Follow instructions to install Anaconda or the more lightweight miniconda.

dev-setup

For detailed instructions, scripts, and tools to set up your development environment for data analysis, check out the dev-setup repo.

running-notebooks

To view interactive content or to modify elements within the IPython notebooks, you must first clone or download the repository then run the notebook. More information on IPython Notebooks can be found here.

$ git clone https://github.com/donnemartin/data-science-ipython-notebooks.git
$ cd data-science-ipython-notebooks
$ jupyter notebook

Notebooks tested with Python 2.7.x.

credits

• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney
• PyCon 2015 Scikit-learn Tutorial by Jake VanderPlas
• Python Data Science Handbook by Jake VanderPlas
• Parallel Machine Learning with scikit-learn and IPython by Olivier Grisel
• Statistical Interference Using Computational Methods in Python by Allen Downey
• TensorFlow Examples by Aymeric Damien
• TensorFlow Tutorials by Parag K Mital
• TensorFlow Tutorials by Nathan Lintz
• TensorFlow Tutorials by Alexander R Johansen
• TensorFlow Book by Nishant Shukla
• Summer School 2015 by mila-udem
• Keras tutorials by Valerio Maggio
• Kaggle
• Yhat Blog

contributing

Contributions are welcome! For bug reports or requests please submit an issue.

contact-info

Feel free to contact me to discuss any issues, questions, or comments.

• Email: donne.martin@gmail.com
• Twitter: @donne_martin
• GitHub: donnemartin
• LinkedIn: donnemartin
• Website: donnemartin.com

license

This repository contains a variety of content; some developed by Donne Martin, and some from third-parties. The third-party content is distributed under the license provided by those parties.

The content developed by Donne Martin is distributed under the following license:

Copyright 2015 Donne Martin

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.