Recommend Colab + GPUs for transfer learning episode

episodes/5-transfer-learning.md

@@ -17,7 +17,7 @@
 
 An example: Let's say that you want to train a model to classify images of different dog breeds. You could make use of a pre-trained network that learned how to classify images of dogs and cats. The pre-trained network will not know anything about different dog breeds, but it will have captured some general knowledge of, on a high-level, what dogs look like, and on a low-level all the different features (eyes, ears, paws, fur) that make up an image of a dog. Further training this model on your dog breed dataset is a much easier task than training from scratch, because the model can use the general knowledge captured in the pre-trained network.
 
 ![](episodes/fig/05-transfer_learning.png)
 <!-- 
 Edit this plot using the Mermaid live editor:
 1. Open this link that includes the source code of the chart to open the live editor web interface:
@@ -30,6 +30,65 @@
 
 In this episode we will learn how use Keras to adapt a state-of-the-art pre-trained model to the [Dollar Street Dataset](https://zenodo.org/records/10970014).
 
+::: spoiler
+### Google Colab + GPUs recommended
+This episode uses a respectably sized neural network — *DenseNet121*, which has 121 layers and over 7 million parameters. Training or "finetuning" this large of a model on a CPU is slow. Graphical Processing Units (GPUs) dramatically accelerate deep learning by speeding up the underlying matrix operations, often achieving **10-100x faster performance** than CPUs.
+
+To speed things up, we recommend using [Google Colab](https://colab.research.google.com/), which provides free access to a GPU. 
+
+#### How to run this episode in Colab:
+
+**A. Upload the `dl_workshop` folder to your Google Drive (excluding the `venv` folder).**  
+
+- This folder should contain the `data/` directory with the `.npy` files used in this episode. If the instructor has provided pre-filled notebooks for the workshop, please upload these as well. DO NOT UPLOAD your virtual environment folder as it is very large, and we'll be using Google Colab's pre-built environment instead.
+
+**B. Start a blank notebook in Colab or open pre-filled notebook provided by instructor**
+
+- Go to [https://colab.research.google.com/](https://colab.research.google.com/), click "New Notebook", and copy/paste code from this episode into cells.
+
+**C. Enable GPU**
+
+- Go to `Runtime > Change runtime type`
+- Set "Hardware accelerator" to `GPU`
+- Click "Save"
+
+**D. Mount your Google Drive in the notebook:**
+
+```python
+from google.colab import drive
+drive.mount('/content/drive')
+```
+**E. Set the data path to point to your uploaded folder, and load data:**
+
+```python
+import pathlib
+import numpy as np
+DATA_FOLDER = pathlib.Path('/content/drive/MyDrive/dl_workshop/data')
+train_images = np.load(DATA_FOLDER / 'train_images.npy')
+val_images = np.load(DATA_FOLDER / 'test_images.npy')
+train_labels = np.load(DATA_FOLDER / 'train_labels.npy')
+val_labels = np.load(DATA_FOLDER / 'test_labels.npy')
+```
+
+**F. Check if GPU is active:**
+
+```python
+import tensorflow as tf
+
+if tf.config.list_physical_devices('GPU'):
+    print("GPU is available and will be used.")
+else:
+    print("GPU not found. Training will use CPU and may be slow.")
+```
+
+```output
+GPU is available and will be used.
+```
+
+Assuming you have installed the GPU-enabled version of TensorFlow (which is pre-installed in Colab), you don't need to do anything else to enable GPU usage during training, tuning, or inference. TensorFlow/Keras will automatically use the GPU whenever it's available and supported. Note — we didn't include the GPU version of Tensorflow in this workshop's virtual environment because it can be finnicky to configure across operating systems, and many learners don't have the appropriate GPU hardware available.
+
+:::
+
 
 ## 1. Formulate / Outline the problem
 
@@ -41,7 +100,8 @@
 import pathlib
 import numpy as np
 
-DATA_FOLDER = pathlib.Path('data/dataset_dollarstreet/') # change to location where you stored the data
+# DATA_FOLDER = pathlib.Path('data/') # local path
+DATA_FOLDER = pathlib.Path('/content/drive/MyDrive/dl_workshop/data') # Colab path
 train_images = np.load(DATA_FOLDER / 'train_images.npy')
 val_images = np.load(DATA_FOLDER / 'test_images.npy')
 train_labels = np.load(DATA_FOLDER / 'train_labels.npy')