From 314e6afbe40b050b63faaac1b07ba5aa00d4ec2b Mon Sep 17 00:00:00 2001
From: Soraya Arias <soraya.arias@inria.fr>
Date: Mon, 22 Jan 2024 19:13:44 +0100
Subject: [PATCH] Update url for keras3 + misc url correction
---
BHPD.Keras3/01-DNN-Regression.ipynb | 10 +++++-----
BHPD.Keras3/02-DNN-Regression-Premium.ipynb | 10 +++++-----
BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb | 6 +++---
Embedding.Keras3/01-One-hot-encoding.ipynb | 8 ++++----
Embedding.Keras3/02-Keras-embedding.ipynb | 8 ++++----
Embedding.Keras3/03-Prediction.ipynb | 2 +-
Embedding.Keras3/05-LSTM-Keras.ipynb | 12 ++++++------
MNIST.Keras3/01-DNN-MNIST.ipynb | 10 +++++-----
MNIST.Keras3/02-CNN-MNIST.ipynb | 10 +++++-----
MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb | 8 ++++----
Perceptron/01-Simple-Perceptron.ipynb | 2 +-
Wine.Keras3/01-DNN-Wine-Regression.ipynb | 10 +++++-----
12 files changed, 48 insertions(+), 48 deletions(-)
diff --git a/BHPD.Keras3/01-DNN-Regression.ipynb b/BHPD.Keras3/01-DNN-Regression.ipynb
index fdb202a..df15889 100644
--- a/BHPD.Keras3/01-DNN-Regression.ipynb
+++ b/BHPD.Keras3/01-DNN-Regression.ipynb
@@ -124,7 +124,7 @@
"## Step 2 - Retrieve data\n",
"\n",
"### 2.1 - Option 1 : From Keras\n",
- "Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) "
+ "Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://keras.io/api/datasets) "
]
},
{
@@ -231,10 +231,10 @@
"source": [
"## Step 4 - Build a model\n",
"About informations about : \n",
- " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
- " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
- " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
- " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ " - [Optimizer](https://keras.io/api/optimizers)\n",
+ " - [Activation](https://keras.io/api/layers/activations)\n",
+ " - [Loss](https://keras.io/api/losses)\n",
+ " - [Metrics](https://keras.io/api/metrics)"
]
},
{
diff --git a/BHPD.Keras3/02-DNN-Regression-Premium.ipynb b/BHPD.Keras3/02-DNN-Regression-Premium.ipynb
index 65cdd8b..626b5e8 100644
--- a/BHPD.Keras3/02-DNN-Regression-Premium.ipynb
+++ b/BHPD.Keras3/02-DNN-Regression-Premium.ipynb
@@ -123,7 +123,7 @@
"## Step 2 - Retrieve data\n",
"\n",
"### 2.1 - Option 1 : From Keras\n",
- "Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) "
+ "Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://keras.io/api/datasets) "
]
},
{
@@ -226,10 +226,10 @@
"source": [
"## Step 4 - Build a model\n",
"More informations about : \n",
- " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
- " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
- " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
- " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ " - [Optimizer](https://keras.io/api/optimizers)\n",
+ " - [Activation](https://keras.io/api/layers/activations)\n",
+ " - [Loss](https://keras.io/api/losses)\n",
+ " - [Metrics](https://keras.io/api/metrics)"
]
},
{
diff --git a/BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb b/BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb
index e75b7ad..b2dcb48 100644
--- a/BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb
+++ b/BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb
@@ -208,7 +208,7 @@
"source": [
"## Step 5 - Train the model\n",
"\n",
- "#### 5.1 - stochastic gradient descent strategy to fit the model"
+ "### 5.1 - Stochastic gradient descent strategy to fit the model"
]
},
{
@@ -262,7 +262,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "##### 5.2 - get the model"
+ "#### 5.2 - Get the model"
]
},
{
@@ -283,7 +283,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "##### 5.3 - train the model"
+ "#### 5.3 - Train the model"
]
},
{
diff --git a/Embedding.Keras3/01-One-hot-encoding.ipynb b/Embedding.Keras3/01-One-hot-encoding.ipynb
index 7c7451c..a66ba60 100644
--- a/Embedding.Keras3/01-One-hot-encoding.ipynb
+++ b/Embedding.Keras3/01-One-hot-encoding.ipynb
@@ -16,7 +16,7 @@
"\n",
"Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
"Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
- "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
+ "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://keras.io/datasets)\n",
"\n",
"## What we're going to do :\n",
"\n",
@@ -177,7 +177,7 @@
"source": [
"## Step 3 - Retrieve data\n",
"\n",
- "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
+ "IMDb dataset can bet get directly from Keras - see [documentation](https://keras.io/api/datasets/imdb/) \n",
"Note : Due to their nature, textual data can be somewhat complex.\n",
"\n",
"### 3.1 - Data structure : \n",
@@ -211,8 +211,8 @@
"metadata": {},
"source": [
"### 3.2 - Load dataset\n",
- "For simplicity, we will use a pre-formatted dataset - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data) \n",
- "However, Keras offers some usefull tools for formatting textual data - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text) \n",
+ "For simplicity, we will use a pre-formatted dataset - See [documentation](https://keras.io/api/datasets/imdb) \n",
+ "However, Keras offers some useful tools for formatting textual data - See [documentation](hhttps://keras.io/api/layers/preprocessing_layers/text/text_vectorization/) \n",
"\n",
"By default : \n",
" - Start of a sequence will be marked with : 1\n",
diff --git a/Embedding.Keras3/02-Keras-embedding.ipynb b/Embedding.Keras3/02-Keras-embedding.ipynb
index 21f5fa0..face9b5 100644
--- a/Embedding.Keras3/02-Keras-embedding.ipynb
+++ b/Embedding.Keras3/02-Keras-embedding.ipynb
@@ -16,7 +16,7 @@
"\n",
"Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
"Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
- "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
+ "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://keras.io/datasets)\n",
"\n",
"## What we're going to do :\n",
"\n",
@@ -114,7 +114,7 @@
"source": [
"## Step 2 - Retrieve data\n",
"\n",
- "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
+ "IMDb dataset can bet get directly from Keras - see [documentation](https://keras.io/api/datasets) \n",
"Note : Due to their nature, textual data can be somewhat complex.\n",
"\n",
"For more details about the management of this dataset, see notebook [IMDB1](01-One-hot-encoding.ipynb)"
@@ -249,8 +249,8 @@
"## Step 4 - Build the model\n",
"\n",
"More documentation about this model functions :\n",
- " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
- " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
+ " - [Embedding](https://keras.io/api/layers/core_layers/embedding/)\n",
+ " - [GlobalAveragePooling1D](https://keras.io/api/layers/pooling_layers/global_average_pooling1d/)"
]
},
{
diff --git a/Embedding.Keras3/03-Prediction.ipynb b/Embedding.Keras3/03-Prediction.ipynb
index ed16420..e6d478c 100644
--- a/Embedding.Keras3/03-Prediction.ipynb
+++ b/Embedding.Keras3/03-Prediction.ipynb
@@ -114,7 +114,7 @@
"metadata": {},
"source": [
"### 2.2 - Retrieve dictionaries\n",
- "Note : This dictionary is generated by [01-Embedding-Keras](01-Embedding-Keras.ipynb) notebook."
+ "Note : This dictionary is generated by [02-Embedding-Keras](02-Keras-embedding.ipynb) notebook."
]
},
{
diff --git a/Embedding.Keras3/05-LSTM-Keras.ipynb b/Embedding.Keras3/05-LSTM-Keras.ipynb
index 620c3c8..b5aa4bf 100644
--- a/Embedding.Keras3/05-LSTM-Keras.ipynb
+++ b/Embedding.Keras3/05-LSTM-Keras.ipynb
@@ -16,7 +16,7 @@
"\n",
"Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
"Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
- "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
+ "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://keras.io/datasets)\n",
"\n",
"## What we're going to do :\n",
"\n",
@@ -111,7 +111,7 @@
"source": [
"## Step 3 - Retrieve data\n",
"\n",
- "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
+ "IMDb dataset can bet get directly from Keras - see [documentation](https://keras.io/api/datasets) \n",
"Note : Due to their nature, textual data can be somewhat complex."
]
},
@@ -120,8 +120,8 @@
"metadata": {},
"source": [
"### 3.1 - Get dataset\n",
- "For simplicity, we will use a pre-formatted dataset - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data) \n",
- "However, Keras offers some usefull tools for formatting textual data - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text) \n",
+ "For simplicity, we will use a pre-formatted dataset - See [documentation](https://keras.io/api/datasets/imdb/) \n",
+ "However, Keras offers some usefull tools for formatting textual data - See [documentation](https://keras.io/api/layers/preprocessing_layers/text/text_vectorization/) \n",
"\n",
"**Load dataset :**"
]
@@ -252,8 +252,8 @@
"## Step 5 - Build the model\n",
"\n",
"More documentation about this model functions :\n",
- " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
- " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
+ " - [Embedding](https://keras.io/api/layers/core_layers/embedding/)\n",
+ " - [GlobalAveragePooling1D](https://keras.io/api/layers/pooling_layers/global_average_pooling1d)"
]
},
{
diff --git a/MNIST.Keras3/01-DNN-MNIST.ipynb b/MNIST.Keras3/01-DNN-MNIST.ipynb
index 81cd2d7..44f9649 100644
--- a/MNIST.Keras3/01-DNN-MNIST.ipynb
+++ b/MNIST.Keras3/01-DNN-MNIST.ipynb
@@ -96,7 +96,7 @@
"source": [
"## Step 2 - Retrieve data\n",
"MNIST is one of the most famous historic dataset. \n",
- "Include in [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)"
+ "Include in [Keras datasets](https://keras.io/datasets)"
]
},
{
@@ -158,10 +158,10 @@
"source": [
"## Step 4 - Create model\n",
"About informations about : \n",
- " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
- " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
- " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
- " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ " - [Optimizer](https://keras.io/api/optimizers)\n",
+ " - [Activation](https://keras.io/api/layers/activations)\n",
+ " - [Loss](https://keras.io/api/losses)\n",
+ " - [Metrics](https://keras.io/api/metrics)"
]
},
{
diff --git a/MNIST.Keras3/02-CNN-MNIST.ipynb b/MNIST.Keras3/02-CNN-MNIST.ipynb
index 19c6b22..014e46f 100644
--- a/MNIST.Keras3/02-CNN-MNIST.ipynb
+++ b/MNIST.Keras3/02-CNN-MNIST.ipynb
@@ -96,7 +96,7 @@
"source": [
"## Step 2 - Retrieve data\n",
"MNIST is one of the most famous historic dataset. \n",
- "Include in [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)"
+ "Include in [Keras datasets](https://keras.io/datasets)"
]
},
{
@@ -161,10 +161,10 @@
"source": [
"## Step 4 - Create model\n",
"About informations about : \n",
- " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
- " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
- " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
- " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ " - [Optimizer](https://keras.io/api/optimizers)\n",
+ " - [Activation](https://keras.io/api/layers/activations)\n",
+ " - [Loss](https://keras.io/api/losses)\n",
+ " - [Metrics](https://keras.io/api/metrics)"
]
},
{
diff --git a/MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb b/MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb
index 94e1c34..674eee5 100644
--- a/MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb
+++ b/MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb
@@ -66,7 +66,7 @@
"source": [
"## Step 2 - Retrieve data\n",
"MNIST is one of the most famous historic dataset. \n",
- "Include in [torchvision datasets](https://pytorch.org/docs/stable/torchvision/datasets.html)"
+ "Include in [torchvision datasets](https://pytorch.org/vision/stable/datasets.html)"
]
},
{
@@ -189,9 +189,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Step 5 - Train the model\n",
+ "## Step 5 - Train the model\n",
"\n",
- "#### 5.1 - stochastic gradient descent strategy to fit the model\n"
+ "### 5.1 - Stochastic gradient descent strategy to fit the model\n"
]
},
{
@@ -246,7 +246,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "##### 5.2 - fit the model"
+ "### 5.2 - Fit the model"
]
},
{
diff --git a/Perceptron/01-Simple-Perceptron.ipynb b/Perceptron/01-Simple-Perceptron.ipynb
index aab34c2..ff37a5a 100644
--- a/Perceptron/01-Simple-Perceptron.ipynb
+++ b/Perceptron/01-Simple-Perceptron.ipynb
@@ -52,7 +52,7 @@
"## Step 2 - Prepare IRIS Dataset\n",
"\n",
"Retrieve a dataset : http://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets \n",
- "About the datesets : http://scikit-learn.org/stable/datasets/index.html \n",
+ "About the datesets : https://scikit-learn.org/stable/datasets.html#datasets \n",
"\n",
"Data fields (X) :\n",
"- 0 : sepal length in cm\n",
diff --git a/Wine.Keras3/01-DNN-Wine-Regression.ipynb b/Wine.Keras3/01-DNN-Wine-Regression.ipynb
index f3abe84..dd9d1c5 100644
--- a/Wine.Keras3/01-DNN-Wine-Regression.ipynb
+++ b/Wine.Keras3/01-DNN-Wine-Regression.ipynb
@@ -18,7 +18,7 @@
"This dataset is provide by : \n",
"Paulo Cortez, University of Minho, Guimarães, Portugal, http://www3.dsi.uminho.pt/pcortez \n",
"A. Cerdeira, F. Almeida, T. Matos and J. Reis, Viticulture Commission of the Vinho Verde Region(CVRVV), Porto, Portugal, @2009 \n",
- "This dataset can be retreive at [University of California Irvine (UCI)](https://archive-beta.ics.uci.edu/ml/datasets/wine+quality)\n",
+ "This dataset can be retreive at [University of California Irvine (UCI)](https://archive.ics.uci.edu/dataset/186/wine+quality)\n",
"\n",
"\n",
"Due to privacy and logistic issues, only physicochemical and sensory variables are available \n",
@@ -202,10 +202,10 @@
"source": [
"## Step 4 - Build a model\n",
"More informations about : \n",
- " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
- " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
- " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
- " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ " - [Optimizer](https://keras.io/api/optimizers)\n",
+ " - [Activation](https://keras.io/api/layers/activations)\n",
+ " - [Loss](https://keras.io/api/losses)\n",
+ " - [Metrics](https://keras.io/api/metrics)"
]
},
{
--
GitLab