v0.11

Moved CUDA compatibility to use CUDA.jl instead of CuArrays.jl
Add kaiming initialization methods: kaiming_uniform and kaiming_normal
Use DataLoader with NamedTuples, so that tensors can be accessed by name.
Error if Dense layers weights and biases are not arrays.
Add Adaptive Pooling in Flux layers.
Change to DataLoader's constructor
Uniform loss interface
Optimistic ADAM (OADAM) optimizer for adversarial training.
Add option for same padding to conv and pooling layers by setting pad=SamePad().
Added option to set bias to Flux.Zeros to eliminating bias from being trained.
Added GlobalMaxPool and GlobalMeanPool layers for performing global pooling operations.
Added ClipValue and ClipNorm in this pr to Flux.Optimise to provide a cleaner API for gradient clipping.
Added new kwarg-only constructors for the various convolutional layers.
Documented the convolutional layer constructors accepting weight and bias keyword arguments to supply custom arrays for those fields.
Testing suite improvements now test for gradients of all layers along with GPU support.
Functors have now moved to Functors.jl to allow for their use outside of Flux.
Added helper functions Flux.convfilter and Flux.depthwiseconvfilter to construct weight arrays for convolutions outside of layer constructors so as to not have to depend on the default layers for custom implementations.
and many more fixes and additions...

v0.10.1 - v0.10.4

See GitHub's releases.

The default AD engine has switched from Tracker to Zygote.jl
- The dependency on Tracker.jl has been removed.
- This means Flux now does not depend on using a specialised TrackedArray type, and can be used with normal Array implementations directly.
- Tracker compatibility is maintained in most common cases, but Zygote will be the preferred AD backend for Flux from now on.
The CUDNN wrappers have been moved from Flux into CuArrays, to allow for better supporting the CUDA backend, and improve user experience, not to mention making Flux lean.
*crossentropy functions now work as expected with CuArrays. PR for bce_loss.
Added clearer docs around training and the Optimiser interface.
Layer initialisations have been improved with a clearer API on how to extend it for other purposes.
Better messaging around CUDA availability, with hooks to initialize the GPU as default where possible.
@treelike has been formalised as a functor, with an effective deprecation.
testmode! is deprecated in favour of istraining

Depthwise convolutional layer API changes from in => mult channel specification to in => out channel specification, and deprecates implicit out constructor.
New SkipConnection, which can be used to train residual neural network architectures.
New RADAM optimiser.

Dropout now has a dims argument for specifying the unbroadcast dimensions.
New ConvTranspose layer.
New Maxout layer
Datasets are now hash verified on download to avoid corruption.
We now zero the initial state for RNNs.
Normalisation can now work on arbitrary dims.
Many docs and bugfixes thanks to @KristofferC and others.
NamedTuples now work like Tuples when doing mapleaves.
New "performance tips" section of the docs.
The training loop is now more readable and better shows how to use the lower-level APIs.
New AlphaDropout.
Data.Iris makes Fisher's Iris dataset available with Iris.labels and Iris.features.
New InstanceNorm, as popularized by Instance Normalization: The Missing Ingredient for Fast Stylization.
New GroupNorm, as described in Group Normalization.
New CrossCor.

AD Changes:

det, logdet and logabsdet now have adjoints.
Support for PermuteDimsArray.
Flux.Tracker is now its own package, in preparation for replacing it with Zygote.

Despite the heroic efforts of scholars and archeologists, pre-0.7 history is lost to the sands of time.