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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.
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IPython Notebook(s) demonstrating deep learning functionality.
Additional TensorFlow 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 |
| 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. |
| 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. |
| 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. |
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96/100 health| Learn about basic multi-GPU computation in TensorFlow. |
| tsf-gviz | Learn about graph visualization in TensorFlow. |
| tsf-lviz | Learn about loss visualization in TensorFlow. |
| 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](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/3.3%20%28Extra%29%20LSTM%20for%20Sentence%20Generation |
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