Train neural network for deep learning

Use `trainNetwork`

to train a convolutional neural network
(ConvNet, CNN), a long short-term memory (LSTM) network, or a bidirectional LSTM
(BiLSTM) network for deep learning classification and regression problems. You can train
a network on either a CPU or a GPU. For image classification and image regression, you
can train using multiple GPUs or in parallel. Using GPU, multi-GPU, and parallel options
requires Parallel
Computing Toolbox™. To use a GPU for deep
learning, you must also have a CUDA^{®} enabled NVIDIA^{®} GPU with compute capability 3.0 or higher. Specify training options, including options for the execution environment,
by using `trainingOptions`

.

trains an network for sequence classification and regression problems (for
example, an LSTM or BiLSTM network), where `net`

= trainNetwork(`sequences`

,`Y`

,`layers`

,`options`

)`sequences`

contains sequence or time series predictors and `Y`

contains
the responses. For classification problems, `Y`

is a
categorical vector or a cell array of categorical sequences. For regression
problems, `Y`

is a matrix of targets or a cell array of
numeric sequences.

trains a network for classification and regression problems. The predictors must
be in the first column of `net`

= trainNetwork(`tbl`

,`responseName`

,`layers`

,`options`

)`tbl`

. The
`responseName`

argument specifies the response variables
in `tbl`

.

[1] Kudo, M., J. Toyama, and M.
Shimbo. "Multidimensional Curve Classification Using Passing-Through Regions."
*Pattern Recognition Letters*. Vol. 20, No. 11–13, pp.
1103–1111.

[2] Kudo, M., J. Toyama, and M.
Shimbo. *Japanese Vowels Data Set*.
https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels

`DAGNetwork`

| `LayerGraph`

| `SeriesNetwork`

| `analyzeNetwork`

| `assembleNetwork`

| `classify`

| `predict`

| `trainingOptions`