Bigdl

New feature

* A new BigDL document website online https://bigdl-project.github.io/, which replace the original BigDL wiki
* Added New Models & Layers
+ TreeLSTM and examples for sentiment analytics
+ convLSTM layer
+ 1D convolution layer
+ Mean Absolute Error (MAE) metrics
+ TimeDistributed Layer
+ VolumetricConvolution(3D convolution)
+ VolumetricMaxPooling
+ RoiPooling layer
+ DiceCoefficient loss
+ bi-recurrent layers
* API change
+ Allow user to set regularization per layer
+ Allow user to set learning rate per layer
+ Add predictClass API for python
+ Add DLEstimator for Spark ML pipeline
+ Add Functional API for model definition
+ Add movie length dataset API
+ Add 4d normalize support
+ Add evaluator API to simplify model test
* Install & Deploy
+ Allow user to install BigDL from pip
+ Support win64 platform
+ A new script to auto pack/distribute python dependency on yarn cluster mode
* Model Save/Load
+ Allow user to save BigDL model as Caffe model file
+ Allow user to load/save some Tensorflow model(cover tensorflow slim APIs)
+ Support save/load model file from/to s3/hdfs
* Optimization
+ Add plateau learning rate schedule
+ Allow user to adjust optimization process based on loss and score
+ Add Exponential learning rate decay
+ Add natural exp decay learning rate schedule
+ Add multistep learning rate policy

Enhancement
1. Optimization method API refactor
2. Allow user to load a Caffe model without pre-defining a BigDL model
3. Optimize Recurrent Layers performance
4. Refine the ML pipeline related API, and add more examples
5. Optimize JoinTable layer performance

**Release Notes**
* API Change
1. **Use bigdl as the top level package name for all bigdl python module**
2. Allow user to change the model in the optimizer
3. Allow user to define a model in python API
4. Allow user to invoke BigDL scala code from python in 3rd prject
5. Allow user to use BigDL random generator in python
6. Allow user to use forward/backward method in python
7. Add BiRnn layer to python
8. Remove useless CriterionTable layer


* Enhancement
1. Load libjmkl.so in the class load phase
2. Support python 3.5
3. Initialize gradient buffer at the start of backward to reduce the memory usage
4. Auto pack python dependency in yarn cluster mode

* Bug Fix
1. Fix optimizer continue without failure after retry maximum number
2. Fix LookupTable python API throw noSuchMethod error
3. Fix an addmv bug for 1x1 matrix
4. Fix lenet python example error
5. Fix python load text file encoding issue
6. Fix HardTanh performance issue
7. Fix data may distribute unevenly in vgg example when input partition is too large
8. Fix a bug in SpatialDilatedConvolution
9. Fix a bug in BCECriterion loss function
10. Fix a bug in Add layer