633 Add tutorial for monai bundle step-by-step start

README.md

@@ -178,17 +178,16 @@ Demonstrates the use of the `ThreadBuffer` class used to generate data batches d
 Illustrate reading NIfTI files and test speed of different transforms on different devices.
 
 **modules**
+#### [engines](./modules/bundles)
+Get started tutorial and concrete training / inference examples for MONAI bundle features.
 #### [engines](./modules/engines)
 Training and evaluation examples of 3D segmentation based on UNet3D and synthetic dataset with MONAI workflows, which contains engines, event-handlers, and post-transforms. And GAN training and evaluation example for a medical image generative adversarial network. Easy run training script uses `GanTrainer` to train a 2D CT scan reconstruction network. Evaluation script generates random samples from a trained network.
 
 The examples are built with MONAI workflows, mainly contain: trainer/evaluator, handlers, post_transforms, etc.
 #### [3d_image_transforms](./modules/3d_image_transforms.ipynb)
 This notebook demonstrates the transformations on volumetric images.
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 #### [2d_inference_3d_volume](./modules/2d_inference_3d_volume.ipynb)
 Tutorial that demonstrates how monai `SlidingWindowInferer` can be used when a 3D volume input needs to be provided slice-by-slice to a 2D model and finally, aggregated into a 3D volume.
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 #### [autoencoder_mednist](./modules/autoencoder_mednist.ipynb)
 This tutorial uses the MedNIST hand CT scan dataset to demonstrate MONAI's autoencoder class. The autoencoder is used with an identity encode/decode (i.e., what you put in is what you should get back), as well as demonstrating its usage for de-blurring and de-noising.
 #### [batch_output_transform](./modules/batch_output_transform.py)