About:
APRILANN toolkit (A Pattern Recognizer In Lua with Artificial Neural Networks). This toolkit incorporates ANN algorithms (as dropout, stacked denoising autoencoders, convolutional neural networks), with other pattern recognition methods as hidden makov models (HMMs) among others.
Changes:

Updated home repository link to follow aprilorg github organization.

Improved serialize/deserialize functions, reimplemented all the serialization
procedure.

Added exceptions support to LuaPkg and APRILANN, allowing to capture C++
errors into Lua code.

Added
set class.

Added
series class.

Added
data_frame class, similar to Python Pandas DataFrame.

Serialization and deserilization have been updated with more robust and
reusable API, implemented in
util.serialize() and util.deserialize()
functions.

Added
matrix.ext.broadcast utility (similar to broadcast in numpy).

Added
ProbablisitcMatrixANNComponent , which allow to implement probabilistic
mixtures of posteriors and/or likelihoods.

Added batch normalization ANN component.

Allowing
matrix.join to add new axis.

Added methods
prod() , cumsum() and cumprod() at matrix classes.

Added methods
count_eq() and count_neq() at matrix classes.

Serializable objects API have been augmented with methods
ctor_name() and
ctor_params() in Lua, refered to luaCtorName() and luaCtorParams() in
C++.

Added
cast.to to dynamic cast C++ objects pushed into Lua, allowing to
convert base class objects into any of its derived classes.

Added
matrix.sparse as valid values for targets in ann.loss.mse and
ann.loss.cross_entropy .

Changed
matrix metamethods __index and __newindex , allowing to use
matrix objects with standard Lua operator[] .

Added
matrix.masked_fill and matrix.masked_copy matrix.

Added
matrix.indexed_fill and matrix.indexed_copy matrix.

Added
ann.components.probabilistic_matrix , and its corresponding
specializations ann.components.left_probabilistic_matrix and
ann.components.right_probabilistic_matrix .

Added operator[] in the right side of matrix operations.

Added
ann.components.transpose .

Added
max_gradients_norm in traianble.supervised_trainer , to avoid
gradients exploding.

Added
ann.components.actf.sparse_logistic a logistic activation function
with sparsity penalty.

Simplified
math.add , math.sub , ... and other math extensions for
reductions, their original behavior can be emulated by using bind function.

Added
bind function to freeze any positional argument of any Lua function.

Function
stats.boot uses multiple_unpack to allow a table of sizes and the
generation of multiple index matrices.

Added
multiple_unpack Lua function.

Added
__tostring metamethod to numeric memory blocks in Lua.

Added
dataset.token.sparse_matrix , a dataset which allow to traverse by rows
a sparse matrix instance.

Added
matrix.sparse.builders.dok , a builder which uses the
DictionaryofKeys format to construct a sparse matrix from scratch.

Added method
data to numeric matrix classes.

Added methods
values , indices , first_index to sparse matrix class.

Fixed bugs when reading bad formed CSV files.

Fixed bugs at statistical distributions.

FloatRGB bug solved on equal (+=, =, ...) operators. This bug affected
ImageRGB operations such as resize.

Solved problems when chaining methods in Lua, some objects end to be garbage
collected.

Improved support of strings in autocompletion (rlcompleter package).

Solved bug at
SparseMatrix<T> when reading it from a file.

Solved bug in
Image<T>::rotate90_cw methods.

Solved bug in
SparseMatrix::toDense() method.
C/C++

Better
LuaTable accessors, using [] operator.

Implementation of matrix
__index , __newindex and __call metamethods in
C++.

Implementation of
matProd() , matCumSum() and matCumProd() functions.

Implementation of
matCountEq() and matCountNeq() functions for
Matrix<T> .

Updated
matrix_ext_operations.h to change API of matrix operations. All
functions have been overloaded to accept an inplace operation and another
version which receives a destination matrix.

Adding iterators to language models.

Added
MatrixScalarMap2 which receives as input2 a SparaseMatrix
instance. This functions needs to be generalized to work with CPU and CUDA.

The method
SparseMatrix<T>::fromDenseMatrix() uses a DOKBuilder object
to build the sparse matrix.

The conversion of a
Matrix<T> into a SparseMatrix<T> has been changed from
a constructor overload to the static method
SparseMatrix<T>::fromDenseMatrix() .

Added support for IPyLua.

Optimized matrix access for confusion matrix.

Minor changes in
class.lua .

Improved binding to avoid multiple object copies when pushing C++ objects.

Added Git commit hash and compilation time.

 Operating System:
Linux,
Mac Os X
 Data Formats:
Ascii,
Binary,
Matlab,
Tab Separated,
Png,
Pnm
 Tags:
Deep Learning,
Machine Learning,
Ann,
Hmms,
Viterbi

