donnemartin/data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
IPython Notebook(s) demonstrating deep learning functionality. Additional TensorFlow tutorials: IPython Notebook(s) demonstrating scikit-learn functionality. IPython Notebook(s) demonstrating statistical inference with SciPy functionality. IPython Notebook(s) demonstrating pandas functionality. IPython Notebook(s) demonstrating matplotlib functionality. IPython Notebook(s) demonstrating NumPy functionality. IPython Notebook(s) demonstrating Python functionality geared towards data analysis. IPython Notebook(s) used in kaggle competitions and business analyses. IPython Notebook(s) demonstrating spark and HDFS functionality. IPython Notebook(s) demonstrating Hadoop MapReduce with mrjob functionality. IPython Notebook(s) demonstrating Amazon Web Services (AWS) and AWS tools functionality. Also check out: IPython Notebook(s) demonstrating various command lines for Linux, Git, etc. IPython Notebook(s) demonstrating miscellaneous functionality. algorithmia | Algorithmia is a marketplace for algorithms. This notebook showcases 4 different algorithms: Face Detection, Content Summarizer, Latent Dirichlet Allocation and Optical Character Recognition.| 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…
Read the full README ↗ · Preview checked 2026-11-01T10:48:29.334Z
Inside the original README — Document outline
- data-science-ipython-notebooks
- Index
- deep-learning
- tensor-flow-tutorials
- tensor-flow-exercises
- theano-tutorials
- keras-tutorials
- deep-learning-misc
- scikit-learn
- statistical-inference-scipy
- pandas
- matplotlib
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the README
References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.
Documentation belongs to its respective authors. Reported project/model license: Other. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
What this repository does
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Repository facts
- Owner
- donnemartin
- Primary language
- Python
- Stars
- 29,386
- Forks
- 7,999
- Open issues + pull requests
- 48
- License
- Other
- Archived
- No
- Default branch
- master
- Created
- 2015-01-23T19:38:29.000Z
- Last push
- 2024-03-20T13:52:34.000Z
Topics and intended use
Owner-supplied topics: aws, big-data, caffe, data-science, deep-learning, hadoop, kaggle, keras, machine-learning, mapreduce, matplotlib, numpy, pandas, python, scikit-learn, scipy, spark, tensorflow, theano
Review the README for scope, installation, examples and limitations. We do not run repository code or certify it.
Evaluate before installing
Review licensing and dependencies, inspect recent commits and unresolved issues, and test in an isolated environment before production use. Stars and forks alone cannot answer these questions.
Source and freshness
Source: GitHub. Metadata observed 2026-11-01T12:19:42.808Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
Related repositories
EbookFoundation/free-programming-books
donnemartin/system-design-primer