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429 symbols
980 edges
57 files
308 documented · 72%
README
data-science-ipython-notebooks
Index
- deep-learning
- 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
- tuanavu/tensorflow-basic-tutorials
| 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](http:/ |
Core symbols most depended-on inside this repo
Itemscalled by 32
scipy/thinkstats2.py
_Underride
called by 14
scipy/thinkplot.py
text
called by 13
scikit-learn/fig_code/figures.py
identity_block
called by 12
deep-learning/keras-tutorial/deep_learning_models/resnet50.py
Set
called by 12
scipy/thinkstats2.py
gauss_weight
called by 9
deep-learning/theano-tutorial/rnn_tutorial/rnn_precompile.py
Incr
called by 9
scipy/thinkstats2.py
Normalize
called by 9
scipy/thinkstats2.py
Shape
Languages
Python100%
Modules by API surface
scipy/thinkstats2.py245 symbols
scipy/thinkplot.py35 symbols
scikit-learn/fig_code/svm_gui.py28 symbols
deep-learning/tensor-flow-examples/input_data.py14 symbols
scikit-learn/fig_code/figures.py13 symbols
deep-learning/theano-tutorial/rnn_tutorial/rnn_precompile.py12 symbols
deep-learning/theano-tutorial/rnn_tutorial/lstm_text.py9 symbols
mapreduce/test_mr_s3_log_parser.py7 symbols
deep-learning/keras-tutorial/data_helpers.py7 symbols
scipy/first.py6 symbols
mapreduce/mr_s3_log_parser.py5 symbols
scipy/nsfg.py4 symbols
For agents
$ claude mcp add data-science-ipython-notebooks \
-- python -m otcore.mcp_server <graph>