docs: Preparing release notes for v1.5.3 [WIP 3]

Signed-off-by: mandar2812 <[email protected]>

Author: mandar2812

docs/releases/mydoc_release_notes_153.md

@@ -4,10 +4,79 @@
 
 ## Additions
 
+
+
 ### Tensorflow Integration
 
- **Package** `dynaml.tensorflow`
+ **Package** `dynaml.tensorflow` 
+ 
+ #### Batch Normalisation
+ 
+ [Batch normalisation](https://arxiv.org/abs/1502.03167) is used to standardize activations of convolutional layers and
+ to speed up training of deep neural nets.
+ 
+ **Usage**
+ 
+ ```scala
+ import io.github.mandar2812.dynaml.tensorflow._
+  
+ val bn = dtflearn.batch_norm("BatchNorm1")
+ 
+ ```
+ 
+ 
+ #### Inception v2
+ 
+ The [_Inception_](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf) architecture, proposed by Google is an important
+ building block of _convolutional neural network_ architectures used in vision applications.
+ 
+ ![inception](https://github.com/transcendent-ai-labs/DynaML/blob/master/docs/images/inception.png)
+ 
+ DynaML now offers the Inception cell as a computational layer. 
+ 
+ **Usage**
+ 
+ ```scala
+ import io.github.mandar2812.dynaml.pipes._
+ import io.github.mandar2812.dynaml.tensorflow._
+ import org.platanios.tensorflow.api._
+ 
+ //Create an RELU activation, given a string name/identifier.
+ val relu_act = DataPipe(tf.learn.ReLU(_))
+ 
+ //Learn 10 filters in each branch of the inception cell
+ val filters = Seq(10, 10, 10, 10)
+ 
+ val inception_cell = dtflearn.inception_unit(
+   channels = 3,  num_filters = filters, relu_act,
+   //Apply batch normalisation after each convolution
+   use_batch_norm = true)(layer_index = 1)
+ 
+ ```
+ 
+ In a subsequent [paper](https://arxiv.org/pdf/1512.00567.pdf), the authors introduced optimizations in the Inception 
+ architecture, known colloquially as _Inception v2_.
+ 
+ In _Inception v2_, larger convolutions (i.e. `3 x 3` and `5 x 5`) are implemented in a factorized manner 
+ to reduce the number of parameters to be learned. For example the `3 x 3` convolution is expressed as a 
+ combination of `1 x 3` and `3 x 1` convolutions.
+ 
+ ![inception](https://github.com/transcendent-ai-labs/DynaML/blob/master/docs/images/conv-fact.png)
+ 
+ Similarly the `5 x 5` convolutions can be expressed a combination of two `3 x 3` convolutions
+ 
+ ![inception](https://github.com/transcendent-ai-labs/DynaML/blob/master/docs/images/conv-fact2.png)
+
+ #### Dynamical Systems: Continuous Time RNN
+ 
+  - Added CTRNN layer: `dtflearn.ctrnn`
+  
+  - Added CTRNN layer with inferable time step: `dtflearn.dctrnn`.
+  
+  - Added a projection layer for CTRNN based models `dtflearn.ts_linear`.
 
+  
+
  **Training Stopping Criteria**
 
  Create common and simple training stop criteria such as.
@@ -43,7 +112,13 @@
      .prefetch(10)
 
  // Create the MLP model.
- val input = tf.learn.Input(UINT8, Shape(-1, dataSet.trainImages.shape(1), dataSet.trainImages.shape(2)))
+ val input = tf.learn.Input(
+   UINT8, 
+   Shape(
+     -1, 
+     dataSet.trainImages.shape(1), 
+     dataSet.trainImages.shape(2))
+ )
 
  val trainInput = tf.learn.Input(UINT8, Shape(-1))
 
@@ -99,64 +174,7 @@
    net_layer_sizes)
 
  ```
- 
- 
- #### Batch Normalisation
- 
- [Batch normalisation](https://arxiv.org/abs/1502.03167) is used to standardize activations of convolutional layers and
- to speed up training of deep neural nets.
- 
- **Usage**
- 
- ```scala
- import io.github.mandar2812.dynaml.tensorflow._
-  
- val bn = dtflearn.batch_norm("BatchNorm1")
- 
- ```
- 
- 
- #### Inception v2
- 
- The [_Inception_](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf) architecture, proposed by Google is an important
- building block of _convolutional neural network_ architectures used in vision applications.
- 
- ![inception](https://github.com/transcendent-ai-labs/DynaML/blob/master/docs/images/inception.png)
- 
- DynaML now offers the Inception cell as a computational layer. 
- 
- **Usage**
- 
- ```scala
- import io.github.mandar2812.dynaml.pipes._
- import io.github.mandar2812.dynaml.tensorflow._
- import org.platanios.tensorflow.api._
- 
- //Create an RELU activation, given a string name/identifier.
- val relu_act = DataPipe(tf.learn.ReLU(_))
- 
- //Learn 10 filters in each branch of the inception cell
- val filters = Seq(10, 10, 10, 10)
- 
- val inception_cell = dtflearn.inception_unit(
-   channels = 3,  num_filters = filters, relu_act,
-   //Apply batch normalisation after each convolution
-   use_batch_norm = true)(layer_index = 1)
- 
- ```
- 
- In a subsequent [paper](https://arxiv.org/pdf/1512.00567.pdf), the authors introduced optimizations in the Inception 
- architecture, known colloquially as _Inception v2_.
- 
- In _Inception v2_, larger convolutions (i.e. `3 x 3` and `5 x 5`) are implemented in a factorized manner 
- to reduce the number of parameters to be learned. For example the `3 x 3` convolution is expressed as a 
- combination of `1 x 3` and `3 x 1` convolutions.
- 
- ![inception](https://github.com/transcendent-ai-labs/DynaML/blob/master/docs/images/conv-fact.png)
- 
- Similarly the `5 x 5` convolutions can be expressed a combination of two `3 x 3` convolutions
- 
- ![inception](https://github.com/transcendent-ai-labs/DynaML/blob/master/docs/images/conv-fact2.png)
+
 
 
 ### 3D Graphics