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[DesireCourse Net] Udemy - Master Deep Learning with TensorFlow in Python
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1. Welcome! Course introduction (Size: 1.44 GB) (Files: 296)
1. Welcome! Course introduction
1. Meet your instructors and why you should study machine learning.mp4
1. Meet your instructors and why you should study machine learning.vtt
2. What does the course cover.mp4
2. What does the course cover.vtt
3. What does the course cover - Quiz.html
10. Gradient descent and learning rates
1. Stochastic gradient descent.mp4
1. Stochastic gradient descent.vtt
2. Gradient descent pitfalls.mp4
2. Gradient descent pitfalls.vtt
3. Momentum.mp4
3. Momentum.vtt
4. Learning rate schedules.mp4
4. Learning rate schedules.vtt
5. Learning rate schedules. A picture.mp4
5. Learning rate schedules. A picture.vtt
6. Adaptive learning rate schedules.mp4
6. Adaptive learning rate schedules.vtt
7. Adaptive moment estimation.mp4
7. Adaptive moment estimation.vtt
11. Preprocessing
1. Preprocessing introduction.mp4
1. Preprocessing introduction.vtt
2. Basic preprocessing.mp4
2. Basic preprocessing.vtt
3. Standardization.mp4
3. Standardization.vtt
4. Dealing with categorical data.mp4
4. Dealing with categorical data.vtt
5. One-hot and binary encoding.mp4
5. One-hot and binary encoding.vtt
12. The MNIST example
1. The dataset.mp4
1. The dataset.vtt
10. MNIST - exercises.html
10.1 MNIST_Exercises_All.html
11. MNIST - solutions.html
11.1 MNIST_Depth_Solution.html
11.10 MNIST_Learning_rate_Part_1_Solution.html
11.11 TensorFlow_MNIST_Activation_functions_Part_1_Solution.html
11.2 MNIST_take_note_of_time_Solution.html
11.3 Width_and_Depth_Solution.html
11.4 MNIST_Learning_rate_Part_2_Solution.html
11.5 MNIST_around_98_percent_accuracy_solution.html
11.6 MNIST_Batch_size_Part_2_Solution.html
11.7 MNIST_Width_Solution.html
11.8 MNIST_Batch_size_Part_1_Solution.html
11.9 MNIST_Activation_functions_Part_2_Solution.html
2. How to tackle the MNIST.mp4
2. How to tackle the MNIST.vtt
3. Importing the relevant packages.mp4
3. Importing the relevant packages.vtt
3.1 TensorFlow_MNIST_with_comments_Part_1.html
4. Outlining the model.mp4
4. Outlining the model.vtt
4.1 TensorFlow_MNIST_with_comments_Part_2.html
5. Declaring the loss and the optimization algorithm.mp4
5. Declaring the loss and the optimization algorithm.vtt
5.1 TensorFlow_MNIST_with_comments_Part_3.html
6. Accuracy of prediction.mp4
6. Accuracy of prediction.vtt
6.1 TensorFlow_MNIST_with_comments_Part_4.html
7. Batching and early stopping.mp4
7. Batching and early stopping.vtt
7.1 TensorFlow_MNIST_with_comments_Part_5.html
8. Learning.mp4
8. Learning.vtt
8.1 TensorFlow_MNIST_with_comments_Part_6.html
9. Discuss the results and test.mp4
9. Discuss the results and test.vtt
9.1 TensorFlow_MNIST_with_comments.html
13. Business case
1. Exploring the dataset and identifying predictors.mp4
1. Exploring the dataset and identifying predictors.vtt
1.1 Audiobooks_data.csv.csv
10. Testing the model.mp4
10. Testing the model.vtt
11. A comment on the homework.mp4
11. A comment on the homework.vtt
11.1 Homework exercise.html
12. Final exercise.html
12.1 Homework exercise.html
2. Outlining the business case solution.mp4
2. Outlining the business case solution.vtt
3. Balancing the dataset.mp4
3. Balancing the dataset.vtt
4. Preprocessing the data.mp4
4. Preprocessing the data.vtt
4.1 Preprocessing.html
5. Preprocessing exercise.html
5.1 Preprocessing exercise.html
6. Create a class for batching.mp4
6. Create a class for batching.vtt
6.1 Class.html
7. Outlining the model.mp4
7. Outlining the model.vtt
7.1 Outlining the model.html
8. Optimizing the algorithm.mp4
8. Optimizing the algorithm.vtt
8.1 Optimizing the algorithm.html
9. Interpreting the result.mp4
9. Interpreting the result.vtt
9.1 Interpreting the result.html
14. Appendix Linear Algebra Fundamentals
1. What is a Matrix.mp4
1. What is a Matrix.vtt
10. Dot Product of Matrices.mp4
10. Dot Product of Matrices.vtt
10.1 Dot Product of Matrices Python Notebook.html
11. Why is Linear Algebra Useful.mp4
11. Why is Linear Algebra Useful.vtt
2. Scalars and Vectors.mp4
2. Scalars and Vectors.vtt
3. Linear Algebra and Geometry.mp4
3. Linear Algebra and Geometry.vtt
4. Scalars, Vectors and Matrices in Python.mp4
4. Scalars, Vectors and Matrices in Python.vtt
4.1 Scalars, Vectors and Matrices Python Notebook.html
5. Tensors.mp4
5. Tensors.vtt
5.1 Tensors Notebook.html
6. Addition and Subtraction of Matrices.mp4
6. Addition and Subtraction of Matrices.vtt
6.1 Addition and Subtraction Python Notebook.html
7. Errors when Adding Matrices.mp4
7. Errors when Adding Matrices.vtt
7.1 Errors when Adding Matrices Python Notebook.html
8. Transpose of a Matrix.mp4
8. Transpose of a Matrix.vtt
8.1 Transpose of a Matrix Python Notebook.html
9. Dot Product of Vectors.mp4
9. Dot Product of Vectors.vtt
9.1 Dot Product Python Notebook.html
15. Conclusion
1. See how much you have learned.mp4
1. See how much you have learned.vtt
2. What’s further out there in the machine and deep learning world.mp4
2. What’s further out there in the machine and deep learning world.vtt
3. An overview of CNNs.mp4
3. An overview of CNNs.vtt
4. How DeepMind uses deep learning.html
5. An overview of RNNs.mp4
5. An overview of RNNs.vtt
6. An overview of non-NN approaches.mp4
6. An overview of non-NN approaches.vtt
16. Bonus lecture
1. Bonus lecture Next steps.html
2. Introduction to neural networks
1. Introduction to neural networks.mp4
1. Introduction to neural networks.vtt
1.1 Course Notes - Section 2.pdf.pdf
10. The linear model. Multiple inputs.mp4
10. The linear model. Multiple inputs.vtt
10.1 Course Notes - Section 2.pdf.pdf
11. The linear model. Multiple inputs - Quiz.html
12. The linear model. Multiple inputs and multiple outputs.mp4
12. The linear model. Multiple inputs and multiple outputs.vtt
12.1 Course Notes - Section 2.pdf.pdf
13. The linear model. Multiple inputs and multiple outputs - Quiz.html
14. Graphical representation.mp4
14. Graphical representation.vtt
14.1 Course Notes - Section 2.pdf.pdf
15. Graphical representation - Quiz.html
16. The objective function.mp4
16. The objective function.vtt
16.1 Course Notes - Section 2.pdf.pdf
17. The objective function - Quiz.html
18. L2-norm loss.mp4
18. L2-norm loss.vtt
18.1 Course Notes - Section 2.pdf.pdf
19. L2-norm loss - Quiz.html
2. Introduction to neural networks - Quiz.html
20. Cross-entropy loss.mp4
20. Cross-entropy loss.vtt
20.1 Course Notes - Section 2.pdf.pdf
21. Cross-entropy loss - Quiz.html
22. One parameter gradient descent.mp4
22. One parameter gradient descent.vtt
22.1 GD-function-example.xlsx.xlsx
22.2 Course Notes - Section 2.pdf.pdf
23. One parameter gradient descent - Quiz.html
24. N-parameter gradient descent.mp4
24. N-parameter gradient descent.vtt
24.1 Course Notes - Section 2.pdf.pdf
25. N-parameter gradient descent - Quiz.html
3. Training the model.mp4
3. Training the model.vtt
3.1 Course Notes - Section 2.pdf.pdf
4. Training the model - Quiz.html
5. Types of machine learning.mp4
5. Types of machine learning.vtt
5.1 Course Notes - Section 2.pdf.pdf
6. Types of machine learning - Quiz.html
7. The linear model.mp4
7. The linear model.vtt
7.1 Course Notes - Section 2.pdf.pdf
8. The linear model - Quiz.html
9. Need Help with Linear Algebra.html
3. Setting up the working environment
1. Setting up the environment - An introduction - Do not skip, please!.mp4
1. Setting up the environment - An introduction - Do not skip, please!.vtt
10. Installing packages - exercise.html
11. Installing packages - solution.html
2. Why Python and why Jupyter.mp4
2. Why Python and why Jupyter.vtt
3. Why Python and why Jupyter - Quiz.html
4. Installing Anaconda.mp4
4. Installing Anaconda.vtt
5. The Jupyter dashboard - part 1.mp4
5. The Jupyter dashboard - part 1.vtt
6. The Jupyter dashboard - part 2.mp4
6. The Jupyter dashboard - part 2.vtt
7. Jupyter Shortcuts.html
7.1 Shortcuts for Jupyter.pdf.pdf
8. The Jupyter dashboard - Quiz.html
9. Installing the TensorFlow package.mp4
9. Installing the TensorFlow package.vtt
4. Minimal example - your first machine learning algorithm
1. Minimal example - part 1.mp4
1. Minimal example - part 1.vtt
1.1 Minimal example Part 1.html
2. Minimal example - part 2.mp4
2. Minimal example - part 2.vtt
2.1 Minimal example - part 2.html
3. Minimal example - part 3.mp4
3. Minimal example - part 3.vtt
3.1 Minimal example - part 3.html
4. Minimal example - part 4.mp4
4. Minimal example - part 4.vtt
4.1 Minimal example - part 4.html
5. Minimal example - Exercises.html
5.1 Minimal_example_Exercise_2_Solution.html
5.10 Minimal_example_Exercise_6_Solution.html
5.2 Minimal_example_Exercise_3.d. Solution.html
5.3 Minimal_example_Exercise_4_Solution.html
5.4 Minimal_example_Exercise_3.b. Solution.html
5.5 Minimal_example_All_Exercises.html
5.6 Minimal_example_Exercise_1_Solution.html
5.7 Minimal_example_Exercise_3.c. Solution.html
5.8 Minimal_example_Exercise_5_Solution.html
5.9 Minimal_example_Exercise_3.a. Solution.html
5. TensorFlow - An introduction
1. TensorFlow outline.mp4
1. TensorFlow outline.vtt
2. TensorFlow intro.mp4
2. TensorFlow intro.vtt
3. Types of file formats in TensorFlow.mp4
3. Types of file formats in TensorFlow.vtt
3.1 TensorFlow Minimal example - Part 1.html
4. Inputs, outputs, targets, weights, biases - model layout.mp4
4. Inputs, outputs, targets, weights, biases - model layout.vtt
4.1 TensorFlow Minimal example - Part 2.html
5. Loss function and gradient descent - introducing optimizers.mp4
5. Loss function and gradient descent - introducing optimizers.vtt
5.1 TensorFlow Minimal example - Part 3.html
6. Model output.mp4
6. Model output.vtt
6.1 TensorFlow - Minimal example complete.html
7. Minimal example - Exercises.html
7.1 TensorFlow_Minimal_Example_Exercise_1_Solution.html
7.2 TensorFlow_Minimal_Example_Exercise_2_3_Solution.html
7.3 TensorFlow_Minimal_Example_Exercise_2_1_Solution.html
7.4 TensorFlow_Minimal_Example_All_Exercises.html
7.5 TensorFlow_Minimal_Example_Exercise_3_Solution.html
7.6 TensorFlow_Minimal_Example_Exercise_2_2_Solution.html
7.7 TensorFlow_Minimal_Example_Exercise_4_Solution.html
7.8 TensorFlow_Minimal_Example_Exercise_2_4_Solution.html
6. Going deeper Introduction to deep neural networks
1. Layers.mp4
1. Layers.vtt
1.1 Course Notes - Section 6.pdf.pdf
2. What is a deep net.mp4
2. What is a deep net.vtt
2.1 Course Notes - Section 6.pdf.pdf
3. Understanding deep nets in depth.mp4
3. Understanding deep nets in depth.vtt
4. Why do we need non-linearities.mp4
4. Why do we need non-linearities.vtt
5. Activation functions.mp4
5. Activation functions.vtt
6. Softmax activation.mp4
6. Softmax activation.vtt
7. Backpropagation.mp4
7. Backpropagation.vtt
8. Backpropagation - visual representation.mp4
8. Backpropagation - visual representation.vtt
7. Backpropagation. A peek into the Mathematics of Optimization
1. Backpropagation. A peek into the Mathematics of Optimization.html
1.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf
8. Overfitting
1. Underfitting and overfitting.mp4
1. Underfitting and overfitting.vtt
2. Underfitting and overfitting - classification.mp4
2. Underfitting and overfitting - classification.vtt
3. Training and validation.mp4
3. Training and validation.vtt
4. Training, validation, and test.mp4
4. Training, validation, and test.vtt
5. N-fold cross validation.mp4
5. N-fold cross validation.vtt
6. Early stopping.mp4
6. Early stopping.vtt
9. Initialization
1. Initialization - Introduction.mp4
1. Initialization - Introduction.vtt
2. Types of simple initializations.mp4
2. Types of simple initializations.vtt
3. Xavier initialization.mp4
3. Xavier initialization.vtt
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