Update README.md

README.md

@@ -30,9 +30,9 @@ The MNIST and CiFAR datasets are made available through the Keras deep learning
 [TIDIGITs](https://catalog.ldc.upenn.edu/ldc93s10) and [ImageNet](http://www.image-net.org/) must be downloaded and pre-processed separately, however pre-trained models for ImageNet (e.g., VGG16, ResNet50) are available through Keras as well.
 
 ### Quantization
-To quantize models, run `ares/experiments/quantize/run.sh`.
+To quantize models, run [`ares/experiments/quantize/run.sh`](./experiments/quantize/run.sh).
 
-The quantization transform [`ares/dl_models/transform/quantize.py`](./dl-models/transform/quantize.py) models fixed-point datatypes for weights. Note that activations and arithmetic operations still have full-precision.
+The [quantization transform](./dl-models/transform/quantize.py) emulates fixed-point datatypes for weights. Note that activations and arithmetic operations still have full-precision.
 
 ### Evaluation
 After training and quantizing models, `ares/experiments/eval/eval.sh` can be used to evaluate the models on the validation and test sets.