diff --git a/README.md b/README.md
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+++ b/README.md
@@ -37,25 +37,21 @@ All dependencies are included in the associated docker images. Docker requiremen
 
 # Tutorials
 
-* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
-* [Transfer Learning](https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning)
-* [Train Single Image](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image)
+* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data)  ** << recommended**
 * [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class)
-* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data)
-
-# Jupyter Notebook
-
-Our Jupyter [notebook](https://colab.research.google.com/github/ultralytics/yolov3/blob/master/examples.ipynb) provides quick training, inference and testing examples.
+* [Google Colab Notebook](https://colab.research.google.com/drive/1G8T-VFxQkjDe4idzN8F-hbIBqkkkQnxw) with quick training, inference and testing examples
+* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
+* [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) 
 
 # Training
 
-**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set.
+**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco2017.sh`. Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set.
 
 **Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`.
 
-**Plot Training:** `from utils import utils; utils.plot_results()` plots training results from `coco_16img.data`, `coco_64img.data`, 2 example datasets available in the `data/` folder, which train and test on the first 16 and 64 images of the COCO2014-trainval dataset.
+**Plot Training:** `from utils import utils; utils.plot_results()`
 
-<img src="https://user-images.githubusercontent.com/26833433/63258271-fe9d5300-c27b-11e9-9a15-95038daf4438.png" width="900">
+<img src="https://user-images.githubusercontent.com/26833433/78175826-599d4800-7410-11ea-87d4-f629071838f6.png" width="900">
 
 ## Image Augmentation