CLN: changed old links to new updated links

Author: Karandeep Grover

10_gpu_benchmarking/Exercise/exercise_fashion_mnist_gpu_benchmarking.ipynb

@@ -252,7 +252,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[Click me to check solution for this exercise](https://github.com/codebasics/py/blob/master/DeepLearningML/10_gpu_benchmarking/Exercise/exercise_solution.ipynb)"
+    "[Click me to check solution for this exercise](https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/main/10_gpu_benchmarking/Exercise/exercise_solution.ipynb)"
    ]
   }
  ],
@@ -277,4 +277,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

14_imbalanced/handling_imbalanced_data_exercise.md

@@ -1,12 +1,12 @@
 #### Exercise: Handling imbalanced data in machine learning
 
-1. Use [this notebook](https://github.com/codebasics/py/blob/master/DeepLearningML/13_imbalanced/handling_imbalanced_data.ipynb) but handle imbalanced data using simple logistic regression from skelarn library. The original notebook using neural network but you need to use sklearn logistic regression or any other classification model and improve the f1-score of minority class using,
+1. Use [this notebook](https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/main/13_imbalanced/handling_imbalanced_data.ipynb) but handle imbalanced data using simple logistic regression from skelarn library. The original notebook using neural network but you need to use sklearn logistic regression or any other classification model and improve the f1-score of minority class using,
     1. Undersampling
     1. Oversampling: duplicate copy
     1. OVersampling: SMOT
     1. Ensemble
 
-    [Solution](https://github.com/codebasics/py/blob/master/DeepLearningML/14_imbalanced/handling_imbalanced_data_exercise_solution_telecom_churn.ipynb)    
+    [Solution](https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/main/14_imbalanced/handling_imbalanced_data_exercise_solution_telecom_churn.ipynb)    
 
 2. Take this dataset for bank customer churn prediction : https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling
     1. Build a deep learning model to predict churn rate at bank

16_cnn_cifar10_small_image_classification/cnn_cifar10_dataset.ipynb

@@ -642,9 +642,9 @@
    "metadata": {},
    "source": [
     "Use CNN to do handwritten digits classification using MNIST dataset. You can use this notebook as a reference:\n",
-    "https://github.com/codebasics/py/blob/master/DeepLearningML/1_digits_recognition/digits_recognition_neural_network.ipynb\n",
+    "https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/main/1_digits_recognition/digits_recognition_neural_network.ipynb\n",
     "\n",
-    "Above we used ANN for digits classification. You need to modify this code to use CNN instead. Check how accuracy improves fast with CNN and figure out how CNN can be a better choice for doing image classification compared to ANN. Once you have worked on this problem on your own, you can check my solution by clicking on this link: [Solution](https://github.com/codebasics/py/blob/master/DeepLearningML/16_cnn_cifar10_small_image_classification/cnn_mnist_exercise_solution.ipynb)"
+    "Above we used ANN for digits classification. You need to modify this code to use CNN instead. Check how accuracy improves fast with CNN and figure out how CNN can be a better choice for doing image classification compared to ANN. Once you have worked on this problem on your own, you can check my solution by clicking on this link: [Solution](https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/main/16_cnn_cifar10_small_image_classification/cnn_mnist_exercise_solution.ipynb)"
    ]
   }
  ],
@@ -669,4 +669,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

3_derivatives/derivatives_exercise.md

@@ -7,7 +7,7 @@ To find answers please refer to derivatives rules at https://www.mathsisfun.com/
 
 ![](derivatives_question.jpg)
 
-[Click me for solution of above problems](https://github.com/codebasics/py/tree/master/DeepLearningML/3_derivatives/derivatives_exercise_solution.md)
+[Click me for solution of above problems](https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/main/3_derivatives/derivatives_exercise_solution.md)
 
 ##### Practice more derivative questions by clicking on link below,
 [Derivative Exercise](https://www.mathopolis.com/questions/q.html?id=6800&t=mif&qs=6800_6801_6802_6803_6804_6805_6806_6807_6808_6809_6810_6811_6812&site=1&ref=2f63616c63756c75732f64657269766174697665732d72756c65732e68746d6c&title=446572697661746976652052756c6573)

4_matrix_math/4_matrix_math.md

@@ -7,7 +7,7 @@
 
 ![](flowers.jpg)
 
-[Click here for solution of 1 and 2](https://github.com/codebasics/py/tree/master/DeepLearningML/4_matrix_math/4_matrix_math_exercise_solution.ipynb)
+[Click here for solution of 1 and 2](https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/main/4_matrix_math/4_matrix_math_exercise_solution.ipynb)
 
 3. Here is some matrix exercise from mathisfun.com. Please click on a link below and do the exercise.
 

5_loss/5_loss_or_cost_function.ipynb

@@ -406,7 +406,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[Solution](https://github.com/codebasics/py/tree/master/DeepLearningML/5_loss/loss_function_exercise_solution.ipynb)"
+    "[Solution](https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/main/5_loss/loss_function_exercise_solution.ipynb)"
    ]
   }
  ],
@@ -431,4 +431,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

8_sgd_vs_gd/gd_and_sgd.ipynb

@@ -679,7 +679,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[Solution](https://github.com/codebasics/py/blob/master/DeepLearningML/8_sgd_vs_gd/mini_batch_gd.ipynb)"
+    "[Solution](https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/main/8_sgd_vs_gd/mini_batch_gd.ipynb)"
    ]
   }
  ],
@@ -704,4 +704,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}