# BigDL-Tutorials

**Repository Path**: mirrors_intel/BigDL-Tutorials

## Basic Information

- **Project Name**: BigDL-Tutorials
- **Description**: Step-by-step Deep Leaning Tutorials on Apache Spark using BigDL
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-12-19
- **Last Updated**: 2026-05-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DISCONTINUATION OF PROJECT #
This project will no longer be maintained by Intel.
Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.
Intel no longer accepts patches to this project.
# Deep Learning Tutorials on Apache Spark using BigDL

Step-by-step Deep Learning Tutorials on Apache Spark using [BigDL](https://github.com/intel-analytics/BigDL/). The tutorials are inspired by [Apache Spark examples](http://spark.apache.org/examples.html), the [Theano Tutorials](https://github.com/Newmu/Theano-Tutorials) and the [Tensorflow tutorials](https://github.com/nlintz/TensorFlow-Tutorials).

### Topics
1. [RDD](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/spark_basics/RDD.ipynb) 
2. [DataFrame](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/spark_basics/DataFrame.ipynb)
3. [SparkSQL](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/spark_basics/spark_sql.ipynb)
4. [StructureStreaming](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/spark_basics/structured_streaming.ipynb)
5. [Forward and backward](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/forward_and_backward.ipynb)
6. [Linear Regression](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/linear_regression.ipynb)
7. [Introduction to MNIST](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/introduction_to_mnist.ipynb)
8. [Logistic Regression](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/logistic_regression.ipynb)
9. [Feedforward Neural Network](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/deep_feed_forward_neural_network.ipynb)
10. [Convolutional Neural Network](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/cnn.ipynb)
11. [Recurrent Neural Network](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/rnn.ipynb)
12. [LSTM](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/lstm.ipynb)
13. [Bi-directional RNN](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/birnn.ipynb)
14. [Auto-encoder](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/neural_networks/autoencoder.ipynb)
15. [Visualizing Learning](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/notebooks/bigdl_features/visualization.ipynb)

### Environment
+ Python 3.5/3.6
+ JDK 8
+ Apache Spark >= 2.2.1
+ Jupyter Notebook 4.1
+ BigDL 0.7.0
+ [Setup env on Mac OS](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/SetupMac.md) / [Setup env on Linux](https://github.com/intel-analytics/BigDL-Tutorials/blob/master/SetupLinux.md)

### Start Jupyter Server
* Run ```pip install BigDL==0.7.0```
* Run ```jupyter notebook --notebook-dir=./ --ip=0.0.0.0 --no-browser```

### Start Toree Kernel to Run Scala Notebooks
* Run ```pip install BigDL==0.7.0```
* Run ```pip install https://dist.apache.org/repos/dist/release/incubator/toree/0.2.0-incubating/toree-pip/toree-0.2.0.tar.gz```
* Run ```./toree_install.sh```
* Run ```jupyter notebook --notebook-dir=./ --ip=0.0.0.0 --no-browser```

## Run Demo
* Open a browser - Suggest Chrome or Firefox or Safari
* Access notebook client at address http://localhost:8888, open the example ipynb files and execute.