consistency of wordings from 'pretrained' to 'pre-trained'

fine-tuning.md

@@ -34,9 +34,9 @@ Train on domain-specific data until unfrozen layers converge | yes | yes
 
 ### Transfer Learning
 
-From [Wikipedia](https://en.wikipedia.org/wiki/Transfer_learning) definition, Transfer learning is a technique in machine learning in which knowledge learned from task is re-used in order to boost performance for some related task.  For working on transfer learning, you start with a pretrained model. A pretrained model is a deep learning model trained on a very large dataset (can be image text etc.).  Most of the times, these pretrained models are huge classification models trained on huge data with numerous number of classes. During the course of training these models eventually learns features and representations to minimize the loss.
+From [Wikipedia](https://en.wikipedia.org/wiki/Transfer_learning) definition, Transfer learning is a technique in machine learning in which knowledge learned from task is re-used in order to boost performance for some related task.  For working on transfer learning, you start with a pre-trained model. A pre-trained model is a deep learning model trained on a very large dataset (can be image text etc.).  Most of the times, these pre-trained models are huge classification models trained on huge data with numerous number of classes. During the course of training these models eventually learns features and representations to minimize the loss.
 
-Hence before starting Transfer Learning, we take out the layers responsible for classification (pen-ultimate layers) and treat that as our feature extractor. We leverage this knowledge coming from the feature extractor (pretrained model) to train a smaller model confined to a very specific domain-specific task.
+Hence before starting Transfer Learning, we take out the layers responsible for classification (pen-ultimate layers) and treat that as our feature extractor. We leverage this knowledge coming from the feature extractor (pre-trained model) to train a smaller model confined to a very specific domain-specific task.
 The key is that "frozen" layers remain unchanged -- retaining the original abilities of the pre-trained model -- and act as general & robust feature extractors.
 
 ```{figure-md} transfer-learning-architecture
@@ -48,11 +48,11 @@ Transfer Learning
 
 **Examples**:
 
-- Computer vision: take [ResNet-50](https://huggingface.co/microsoft/resnet-50) -- which was pretrained on the [ImageNet](TODO:link) dataset -- and replace the last layer with an object-detecting model (e.g. TODO model). This object-detecting model can now be trained to e.g. classify [cats-vs-dogs](https://huggingface.co/datasets/cats_vs_dogs).
+- Computer vision: take [ResNet-50](https://huggingface.co/microsoft/resnet-50) -- which was pre-trained on the [ImageNet](TODO:link) dataset -- and replace the last layer with an object-detecting model (e.g. TODO model). This object-detecting model can now be trained to e.g. classify [cats-vs-dogs](https://huggingface.co/datasets/cats_vs_dogs).
 - Natural language processing: take [BERT](https://huggingface.co/google/bert_uncased_L-2_H-768_A-12) -- trained on TODO -- and replace the last layer with TODO. The final layers can be trained on the [tweet sentiment classification dataset](https://huggingface.co/datasets/carblacac/twitter-sentiment-analysis).
 
 **Use cases**:
-`NOTE`: We can even extend the process of transfer learning by unfreezing some layers of pretrained model and retraining them along with our smaller model. This additional step helps the model to adapt on newer domain-specific task or out of distribution tasks.
+`NOTE`: We can even extend the process of transfer learning by unfreezing some layers of pre-trained model and retraining them along with our smaller model. This additional step helps the model to adapt on newer domain-specific task or out of distribution tasks.
 
 - Limited data: when domain-specific dataset size is small, a large model cannot be trained end-to-end without overfitting. However if the model is mostly a frozen general feature extractor, then the subsequent trainable layers are less likely to overfit.
 - Limited compute and time: retraining a large model from scratch requires a lot of compute resources and time. This is unnecessary if similar performance can be achieved through transfer learning (training just part of a large model).
@@ -64,13 +64,13 @@ The key difference here is none (or few) of the pre-trained model's weights are
 
 Transfer learning is very much useful when we have the following constrains
 
-1. Limited data: Transfer learning is a useful solution when our dataset size i small. There we can leverage the knowledge from pretrained model and use that (extracted feature) to fit on our smaller task specific dataset.
+1. Limited data: Transfer learning is a useful solution when our dataset size i small. There we can leverage the knowledge from pre-trained model and use that (extracted feature) to fit on our smaller task specific dataset.
 2. Training efficiency: Transfer learning is very useful when we are constrained with compute resources. Retraining the model from scratch can be very resource intensive. However the same performance of the model can be achieved through transfer learning without using much compute resource. Hence the training time is also very small compared to retraining the model.
 
 
 ### Fine-Tuning
 
-From [Wikipedia’s](https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)) definition, Fine-tuning is an approach to transfer learning in which weights of a pre-trained model is trained on a new data.  In some case we retrain the whole model on our domain-specific dataset or in other cases, we just fine-tune on only a subset of the layers. Through fine-tuning, we are adapting our existing pretrained model on a task-specific dataset.
+From [Wikipedia’s](https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)) definition, Fine-tuning is an approach to transfer learning in which weights of a pre-trained model is trained on a new data.  In some case we retrain the whole model on our domain-specific dataset or in other cases, we just fine-tune on only a subset of the layers. Through fine-tuning, we are adapting our existing pre-trained model on a task-specific dataset.
 
 ```{figure-md} fine-tuning-architecture
 :class: caption