dhuntsperger notebook sync (google#263)

* Sync notebooks

* sync LoRA notebook

* Fix notebook URLs

* add dependency to distributed tuning notebook

---------

Co-authored-by: Mark McDonald <[email protected]>

Author: pcoet

site/en/docs/semantic_retriever.ipynb

@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {

site/en/examples/anomaly_detection.ipynb

@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {

site/en/examples/chat_calculator.ipynb

@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {

site/en/examples/clustering_with_embeddings.ipynb

@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
@@ -37,7 +37,7 @@
         "id": "xPixuBZFck9b"
       },
       "source": [
-        "# Visualizing embeddings with t-SNE\n"
+        "# Visualizing embeddings with t-SNE"
       ]
     },
     {
@@ -1345,7 +1345,7 @@
         "id": "JB3bsmi4Iuak"
       },
       "source": [
-        "You will apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) approach to perform dimensionality reduction. This technique reduces the number of dimensions, while preserving clusters (points that are close together stay close together). For the original data, the model tries to construct a distribution over which other data points are \"neighbors\" (e.g., they share a similar meaning). It then optimizes an objective function to keep a similar distribution in the visualization.\n"
+        "You will apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) approach to perform dimensionality reduction. This technique reduces the number of dimensions, while preserving clusters (points that are close together stay close together). For the original data, the model tries to construct a distribution over which other data points are \"neighbors\" (e.g., they share a similar meaning). It then optimizes an objective function to keep a similar distribution in the visualization."
       ]
     },
     {

site/en/examples/doc_search_emb.ipynb

@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
@@ -192,7 +192,7 @@
       "source": [
         "## Embedding generation\n",
         "\n",
-        "In this section, you will see how to generate embeddings for a piece of text using the embeddings from the Gemini API.\n"
+        "In this section, you will see how to generate embeddings for a piece of text using the embeddings from the Gemini API."
       ]
     },
     {

site/en/examples/text_calculator.ipynb

@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {

site/en/examples/train_text_classifier_embeddings.ipynb

@@ -6,7 +6,7 @@
         "id": "Tce3stUlHN0L"
       },
       "source": [
-        "##### Copyright 2023 Google LLC."
+        "##### Copyright 2024 Google LLC."
       ]
     },
     {
@@ -1356,7 +1356,7 @@
       "source": [
         "## Evaluate model performance\n",
         "\n",
-        "Use Keras `Model.evaluate` to get the loss and accuracy on the test dataset."
+        "Use Keras <a href=\"https://www.tensorflow.org/api_docs/python/tf/keras/Model#evaluate\"><code>Model.evaluate</code></a> to get the loss and accuracy on the test dataset."
       ]
     },
     {