Add Hugging Face and MONAI as hubs for pretrained models

episodes/2-keras.Rmd

@@ -76,10 +76,10 @@
 The `palmerpenguins` data contains size measurements for three penguin species observed on three islands in the Palmer Archipelago, Antarctica.
 The physical attributes measured are flipper length, beak length, beak width, body mass, and sex.
 
 ![*Artwork by @allison_horst*][palmer-penguins]
 
 
 ![*Artwork by @allison_horst*][penguin-beaks]
 
 
 These data were collected from 2007 - 2009 by Dr. Kristen Gorman with the [Palmer Station Long Term Ecological Research Program](https://pal.lternet.edu/), part of the [US Long Term Ecological Research Network](https://lternet.edu/). The data were imported directly from the [Environmental Data Initiative](https://environmentaldatainitiative.org/) (EDI) Data Portal, and are available for use by CC0 license ("No Rights Reserved") in accordance with the [Palmer Station Data Policy](https://pal.lternet.edu/data/policies).
@@ -140,7 +140,7 @@
 sns.pairplot(penguins, hue="species")
 ```
 
 ![][pairplot]
 
 ::: challenge
 
@@ -165,7 +165,7 @@
 sns.pairplot(penguins, hue='sex')
 ```
 
 ![][sex_pairplot]
 
 You see that for each species females have smaller bills and flippers, as well as a smaller body mass.
 You would need a combination of the species and the numerical features to successfully distinguish males from females.
@@ -464,7 +464,7 @@
 ### Choose a pretrained model
 If your data and problem is very similar to what others have done, you can often use a *pretrained network*.
 Even if your problem is different, but the data type is common (for example images), you can use a pretrained network and finetune it for your problem.
-A large number of openly available pretrained networks can be found in the [Model Zoo](https://modelzoo.co/), [pytorch hub](https://pytorch.org/hub/) or [tensorflow hub](https://www.tensorflow.org/hub/).
+A large number of openly available pretrained networks can be found on [Hugging Face](https://huggingface.co/models) (especially LLMs), [MONAI](https://monai.io/) (medical imaging), the [Model Zoo](https://modelzoo.co/), [pytorch hub](https://pytorch.org/hub/) or [tensorflow hub](https://www.tensorflow.org/hub/).
 
 
 ## 5. Choose a loss function and optimizer
@@ -524,7 +524,7 @@
 ```python
 sns.lineplot(x=history.epoch, y=history.history['loss'])
 ```
 ![][training_curve]
 
 This plot can be used to identify whether the training is well configured or whether there
 are problems that need to be addressed.
@@ -543,7 +543,7 @@
 
 3. (optional) Something went wrong here during training. What could be the problem, and how do you see that in the training curve?
 Also compare the range on the y-axis with the previous training curve.
 ![](../fig/02_bad_training_history_1.png){alt='Very jittery training curve with the loss value jumping back and forth between 2 and 4. The range of the y-axis is from 2 to 4, whereas in the previous training curve it was from 0 to 2. The loss seems to decrease a litle bit, but not as much as compared to the previous plot where it dropped to almost 0. The minimum loss in the end is somewhere around 2.'}
 
 :::: solution
 ## Solution
@@ -683,7 +683,7 @@
 ```python
 sns.heatmap(confusion_df, annot=True)
 ```
 ![][confusion_matrix]
 
 ::: challenge
 ## Confusion Matrix