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+ 11 - 1
hpc_ai/ai_science_cfd/English/python/jupyter_notebook/Intro_to_DL/CNN's.ipynb

@@ -53,6 +53,8 @@
     "\n",
     "\n",
     "![alt_text](images/cnn.jpeg)\n",
     "![alt_text](images/cnn.jpeg)\n",
     "\n",
     "\n",
+    "*Source: https://fr.mathworks.com/solutions/deep-learning/convolutional-neural-network.html*\n",
+    "\n",
     "Each input image will pass it through a series of convolution layers with filters (Kernels), pooling, fully connected layers (FC) and apply Softmax function to classify an object with probabilistic values between 0 and 1. \n",
     "Each input image will pass it through a series of convolution layers with filters (Kernels), pooling, fully connected layers (FC) and apply Softmax function to classify an object with probabilistic values between 0 and 1. \n",
     "\n",
     "\n",
     "Let us discuss in brief about the following in detail : \n",
     "Let us discuss in brief about the following in detail : \n",
@@ -70,10 +72,14 @@
     "\n",
     "\n",
     "![alt_text](images/conv.gif)\n",
     "![alt_text](images/conv.gif)\n",
     "\n",
     "\n",
+    "*Source: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53*\n",
+    "\n",
     "We have seen how the convolution operation works, and now let us now see how convolution operation is carried out with multiple layers.\n",
     "We have seen how the convolution operation works, and now let us now see how convolution operation is carried out with multiple layers.\n",
     "\n",
     "\n",
     "![alt_text](images/conv_depth.png)\n",
     "![alt_text](images/conv_depth.png)\n",
     "\n",
     "\n",
+    "*Source: https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215*\n",
+    "\n",
     "Let us define the terms :\n",
     "Let us define the terms :\n",
     "\n",
     "\n",
     "- Hin : Height dimension of the layer\n",
     "- Hin : Height dimension of the layer\n",
@@ -113,6 +119,8 @@
     "\n",
     "\n",
     "![alt_text](images/max_pool.png)\n",
     "![alt_text](images/max_pool.png)\n",
     "\n",
     "\n",
+    "*Source: https://www.programmersought.com/article/47163598855/*\n",
+    "\n",
     "#### Fully Connected Layer :\n",
     "#### Fully Connected Layer :\n",
     "\n",
     "\n",
     "We will then flatten the output from the convolutions layers and feed into it a _Fully Connected layer_ to generate a prediction. The fully connected layer is an ANN Model whose inputs are the features of the Inputs obtained from the Convolutions Layers. \n",
     "We will then flatten the output from the convolutions layers and feed into it a _Fully Connected layer_ to generate a prediction. The fully connected layer is an ANN Model whose inputs are the features of the Inputs obtained from the Convolutions Layers. \n",
@@ -133,6 +141,8 @@
     "<td> <img src=\"images/convtranspose_conv.gif\" alt=\"Drawing\" style=\"width: 500px;\"/> </td>\n",
     "<td> <img src=\"images/convtranspose_conv.gif\" alt=\"Drawing\" style=\"width: 500px;\"/> </td>\n",
     "</tr></table>\n",
     "</tr></table>\n",
     "\n",
     "\n",
+    "*Source https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215*\n",
+    "\n",
     "Tranposed Convolution can also be visualised as Convolution of a Layer with 2x2 padding as displayed in the right gif.\n",
     "Tranposed Convolution can also be visualised as Convolution of a Layer with 2x2 padding as displayed in the right gif.\n",
     "\n",
     "\n",
     "\n",
     "\n",
@@ -505,7 +515,7 @@
    "cell_type": "markdown",
    "cell_type": "markdown",
    "metadata": {},
    "metadata": {},
    "source": [
    "source": [
-    "## References : \n",
+    "## Acknowledgement : \n",
     "\n",
     "\n",
     "\n",
     "\n",
     "[Transposed Convolutions explained](https://medium.com/apache-mxnet/transposed-convolutions-explained-with-ms-excel-52d13030c7e8)\n",
     "[Transposed Convolutions explained](https://medium.com/apache-mxnet/transposed-convolutions-explained-with-ms-excel-52d13030c7e8)\n",

+ 3 - 1
hpc_ai/ai_science_cfd/English/python/jupyter_notebook/Intro_to_DL/Part_2.ipynb

@@ -72,7 +72,9 @@
     "\n",
     "\n",
     "We will be using the **F-MNIST ( Fashion MNIST )** dataset, which is a very popular dataset. This dataset contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels).\n",
     "We will be using the **F-MNIST ( Fashion MNIST )** dataset, which is a very popular dataset. This dataset contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels).\n",
     "\n",
     "\n",
-    "<img src=\"images/fashion-mnist.png\" alt=\"Fashion MNIST sprite\"  width=\"600\">"
+    "<img src=\"images/fashion-mnist.png\" alt=\"Fashion MNIST sprite\"  width=\"600\">\n",
+    "\n",
+    "*Source: https://www.tensorflow.org/tutorials/keras/classification*"
    ]
    ]
   },
   },
   {
   {

+ 13 - 1
hpc_ai/ai_science_climate/English/python/jupyter_notebook/Intro_to_DL/CNN's.ipynb

@@ -48,6 +48,8 @@
     "\n",
     "\n",
     "![alt_text](images/cnn.jpeg)\n",
     "![alt_text](images/cnn.jpeg)\n",
     "\n",
     "\n",
+    "*Source: https://fr.mathworks.com/solutions/deep-learning/convolutional-neural-network.html*\n",
+    "\n",
     "Each input image will pass it through a series of convolution layers with filters (Kernels), pooling, fully connected layers (FC) and apply Softmax function to classify an object with probabilistic values between 0 and 1. \n",
     "Each input image will pass it through a series of convolution layers with filters (Kernels), pooling, fully connected layers (FC) and apply Softmax function to classify an object with probabilistic values between 0 and 1. \n",
     "\n",
     "\n",
     "Let us discuss in brief about the following in detail : \n",
     "Let us discuss in brief about the following in detail : \n",
@@ -65,10 +67,15 @@
     "\n",
     "\n",
     "![alt_text](images/conv.gif)\n",
     "![alt_text](images/conv.gif)\n",
     "\n",
     "\n",
+    "*Source: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53*\n",
+    "\n",
     "We have seen how the convolution operation works, and now let us now see how convolution operation is carried out with multiple layers.\n",
     "We have seen how the convolution operation works, and now let us now see how convolution operation is carried out with multiple layers.\n",
     "\n",
     "\n",
     "![alt_text](images/conv_depth.png)\n",
     "![alt_text](images/conv_depth.png)\n",
     "\n",
     "\n",
+    "*Source: https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215*\n",
+    "\n",
+    "\n",
     "Let us define the terms :\n",
     "Let us define the terms :\n",
     "\n",
     "\n",
     "- Hin : Height dimension of the layer\n",
     "- Hin : Height dimension of the layer\n",
@@ -108,6 +115,8 @@
     "\n",
     "\n",
     "![alt_text](images/max_pool.png)\n",
     "![alt_text](images/max_pool.png)\n",
     "\n",
     "\n",
+    "*Source: https://www.programmersought.com/article/47163598855/*\n",
+    "\n",
     "#### Fully Connected Layer :\n",
     "#### Fully Connected Layer :\n",
     "\n",
     "\n",
     "We will then flatten the output from the convolutions layers and feed into it a _Fully Connected layer_ to generate a prediction. The fully connected layer is an ANN Model whose inputs are the features of the Inputs obtained from the Convolutions Layers. \n",
     "We will then flatten the output from the convolutions layers and feed into it a _Fully Connected layer_ to generate a prediction. The fully connected layer is an ANN Model whose inputs are the features of the Inputs obtained from the Convolutions Layers. \n",
@@ -128,6 +137,9 @@
     "<td> <img src=\"images/convtranspose_conv.gif\" alt=\"Drawing\" style=\"width: 500px;\"/> </td>\n",
     "<td> <img src=\"images/convtranspose_conv.gif\" alt=\"Drawing\" style=\"width: 500px;\"/> </td>\n",
     "</tr></table>\n",
     "</tr></table>\n",
     "\n",
     "\n",
+    "\n",
+    "*Source https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215*\n",
+    "\n",
     "Tranposed Convolution can also be visualised as Convolution of a Layer with 2x2 padding as displayed in the right gif.\n",
     "Tranposed Convolution can also be visualised as Convolution of a Layer with 2x2 padding as displayed in the right gif.\n",
     "\n",
     "\n",
     "\n",
     "\n",
@@ -502,7 +514,7 @@
    "source": [
    "source": [
     "\n",
     "\n",
     "\n",
     "\n",
-    "## References : \n",
+    "## Acknowledgements : \n",
     "\n",
     "\n",
     "\n",
     "\n",
     "[Transposed Convolutions explained](https://medium.com/apache-mxnet/transposed-convolutions-explained-with-ms-excel-52d13030c7e8)\n",
     "[Transposed Convolutions explained](https://medium.com/apache-mxnet/transposed-convolutions-explained-with-ms-excel-52d13030c7e8)\n",

+ 3 - 1
hpc_ai/ai_science_climate/English/python/jupyter_notebook/Intro_to_DL/Part_2.ipynb

@@ -71,7 +71,9 @@
     "\n",
     "\n",
     "We will be using the **F-MNIST ( Fashion MNIST )** dataset, which is a very popular dataset. This dataset contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels).\n",
     "We will be using the **F-MNIST ( Fashion MNIST )** dataset, which is a very popular dataset. This dataset contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels).\n",
     "\n",
     "\n",
-    "<img src=\"images/fashion-mnist.png\" alt=\"Fashion MNIST sprite\"  width=\"600\">"
+    "<img src=\"images/fashion-mnist.png\" alt=\"Fashion MNIST sprite\"  width=\"600\">\n",
+    "\n",
+    "*Source: https://www.tensorflow.org/tutorials/keras/classification*"
    ]
    ]
   },
   },
   {
   {

+ 2 - 0
hpc_ai/ai_science_climate/English/python/jupyter_notebook/Tropical_Cyclone_Intensity_Estimation/Approach_to_the_Problem_&_Inspecting_and_Cleaning_the_Required_Data.ipynb

@@ -175,6 +175,8 @@
     "\n",
     "\n",
     "<td><img src=\"images/grad.jpg\" alt=\"Drawing\" style=\"width: 420px;\"/></td>\n",
     "<td><img src=\"images/grad.jpg\" alt=\"Drawing\" style=\"width: 420px;\"/></td>\n",
     "\n",
     "\n",
+    "*Source: https://towardsdatascience.com/linear-regression-using-gradient-descent-97a6c8700931*\n",
+    "\n",
     "GD runs through all the samples in training set to do a single update for a parameter in a particular iteration. In SGD, on the other hand, you use only one or subset of training sample from your training set to do the update for a parameter in a particular iteration. \n",
     "GD runs through all the samples in training set to do a single update for a parameter in a particular iteration. In SGD, on the other hand, you use only one or subset of training sample from your training set to do the update for a parameter in a particular iteration. \n",
     "\n",
     "\n",
     "Using SGD will be faster because only one training sample is used and it starts improving itself right away from the first sample.\n",
     "Using SGD will be faster because only one training sample is used and it starts improving itself right away from the first sample.\n",