Next: release candidate

The next release packages up 400+ commits by 18 authors. Thanks all!

- #973 by @shelhamer, @jeffdonahue, @Yangqing, @longjon: documentation in both tutorial and developer API form.
- #917, #1110 by @sergeyk standard model format and model zoo clearinghouse for sharing models, with the NIN (network-in-network) and VGG Devil models coming soon.
- #1046 by @shelhamer: cuDNN acceleration for 1.2-3x layer-wise speedups in many cases.
- #741 by @qipeng: add Adaptive Gradient (AdaGrad) and Nesterov's Accelerated Gradient (NAG) solvers while #855 by @jeffdonahue prepared tests for the standard SGD solver already included
- #623 by @akosiorek and @bhack: CMake build system
- #686 by @jeffdonahue: generalization of loss allowing for multiple loss functions with different weights
- #594 by @longjon: on-the-fly net input resizing that lets nets change their input dimensions in-place.
- #940 by @ronghanghu: channel-wise softmax for multiple outputs.
- #846 by @qipeng: mean-variance normalization layer.
- #688, #1053 by @to3i, @jeffdonahue: elementwise max by `EltwiseLayer` operation.
- #954, #995 by @geenux, @kloudkl: simplify and de-duplicate data layers
- #1000 by @kloudkl: migrate to boost::thread
- various documentation and example improvements from the community -- thanks!

DOCUMENTATION: there is [tutorial documentation](http://caffe.berkeleyvision.org/tutorial/) and [developer API documentation](http://caffe.berkeleyvision.org/doxygen/) courtesy of Doxygen (thanks to Jeff!). The documentation, in particular the developer API, is still in-progress so come help and join the comment crusade!

DEPENDENCIES: CUDA 6.5 is the suggested version. [cuDNN](https://developer.nvidia.com/cudnn) is an acceleration library for deep network operations with drop-in integration to Caffe. It is not required but suggested for best performance. See #1046.

DEPRECATION: transformation parameters now have their own configuration message to reduce duplication across the data layers. For instance

    layers {
      name: "mnist"
      type: DATA
      top: "data"
      top: "label"
      data_param {
        source: "examples/mnist/mnist_train_lmdb"
        backend: LMDB
        batch_size: 64
      }
      transform_param {
        scale: 0.00390625
      }
    }

is now the proper format with the `transform_param` block. Old models are currently automagically upgraded but **you should upgrade with the included tools** `upgrade_net_proto_{text,binary}`.