[DesireCourse.Net] Udemy - Deep Learning with TensorFlow 2.0 [2020]

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Last Access Time	Sept. 8, 2024, 11:10 p.m.
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14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4 144.3 MB
1. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.mp4 105.8 MB
13. Business case/4. Preprocessing the data.mp4 84.3 MB
13. Business case/1. Exploring the dataset and identifying predictors.mp4 66.3 MB
13. Business case/9. Setting an early stopping mechanism.mp4 49.8 MB
14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.mp4 49.8 MB
14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.mp4 49.4 MB
12. The MNIST example/6. Preprocess the data - shuffle and batch the data.mp4 41.6 MB
12. The MNIST example/10. Learning.mp4 41.0 MB
2. Introduction to neural networks/24. N-parameter gradient descent.mp4 39.4 MB
3. Setting up the working environment/9. Installing TensorFlow 2.mp4 38.7 MB
2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp4 38.3 MB
14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.mp4 38.1 MB
5. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 34.7 MB
14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.mp4 33.8 MB
14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp4 33.6 MB
5. TensorFlow - An introduction/1. TensorFlow outline.mp4 33.5 MB
14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp4 32.6 MB
3. Setting up the working environment/2. Why Python and why Jupyter.mp4 32.1 MB
13. Business case/8. Learning and interpreting the result.mp4 31.2 MB
13. Business case/3. Balancing the dataset.mp4 30.4 MB
5. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.mp4 30.3 MB
12. The MNIST example/13. Testing the model.mp4 29.5 MB
12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.mp4 29.1 MB
3. Setting up the working environment/4. Installing Anaconda.mp4 28.4 MB
12. The MNIST example/8. Outline the model.mp4 28.3 MB
14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.mp4 26.7 MB
14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp4 24.0 MB
5. TensorFlow - An introduction/7. Cutomizing your model.mp4 22.9 MB
14. Appendix Linear Algebra Fundamentals/5. Tensors.mp4 22.5 MB
5. TensorFlow - An introduction/2. TensorFlow 2 intro.mp4 22.0 MB
4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp4 20.8 MB
3. Setting up the working environment/6. The Jupyter dashboard - part 2.mp4 18.8 MB
12. The MNIST example/2. How to tackle the MNIST.mp4 18.7 MB
2. Introduction to neural networks/22. One parameter gradient descent.mp4 17.8 MB
13. Business case/6. Load the preprocessed data.mp4 17.6 MB
5. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.mp4 16.4 MB
1. Welcome! Course introduction/2. What does the course cover.mp4 16.4 MB
12. The MNIST example/3. Importing the relevant packages and load the data.mp4 16.3 MB
15. Conclusion/1. See how much you have learned.mp4 14.0 MB
12. The MNIST example/9. Select the loss and the optimizer.mp4 13.9 MB
2. Introduction to neural networks/1. Introduction to neural networks.mp4 13.6 MB
6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp4 13.4 MB
12. The MNIST example/1. The dataset.mp4 13.4 MB
2. Introduction to neural networks/5. Types of machine learning.mp4 12.2 MB
2. Introduction to neural networks/20. Cross-entropy loss.mp4 11.3 MB
14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.mp4 11.2 MB
6. Going deeper Introduction to deep neural networks/7. Backpropagation.mp4 11.1 MB
8. Overfitting/1. Underfitting and overfitting.mp4 11.0 MB
15. Conclusion/3. An overview of CNNs.mp4 10.9 MB
13. Business case/11. Testing the model.mp4 10.8 MB
4. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.mp4 10.7 MB
10. Gradient descent and learning rates/4. Learning rate schedules.mp4 10.3 MB
4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp4 9.8 MB
8. Overfitting/6. Early stopping.mp4 9.4 MB
10. Gradient descent and learning rates/1. Stochastic gradient descent.mp4 9.4 MB
8. Overfitting/3. Training and validation.mp4 9.2 MB
2. Introduction to neural networks/7. The linear model.mp4 9.1 MB
6. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.mp4 9.0 MB
10. Gradient descent and learning rates/6. Adaptive learning rate schedules.mp4 8.9 MB
2. Introduction to neural networks/3. Training the model.mp4 8.8 MB
6. Going deeper Introduction to deep neural networks/5. Activation functions.mp4 8.7 MB
3. Setting up the working environment/5. The Jupyter dashboard - part 1.mp4 8.7 MB
11. Preprocessing/1. Preprocessing introduction.mp4 8.4 MB
11. Preprocessing/3. Standardization.mp4 8.3 MB
9. Initialization/1. Initialization - Introduction.mp4 8.0 MB
15. Conclusion/6. An overview of non-NN approaches.mp4 7.8 MB
10. Gradient descent and learning rates/7. Adaptive moment estimation.mp4 7.8 MB
2. Introduction to neural networks/10. The linear model. Multiple inputs.mp4 7.5 MB
8. Overfitting/4. Training, validation, and test.mp4 7.4 MB
6. Going deeper Introduction to deep neural networks/6. Softmax activation.mp4 7.4 MB
13. Business case/2. Outlining the business case solution.mp4 7.3 MB
2. Introduction to neural networks/18. L2-norm loss.mp4 7.3 MB
8. Overfitting/5. N-fold cross validation.mp4 7.0 MB
6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.mp4 6.8 MB
8. Overfitting/2. Underfitting and overfitting - classification.mp4 6.8 MB
5. TensorFlow - An introduction/3. A Note on Coding in TensorFlow.mp4 6.8 MB
6. Going deeper Introduction to deep neural networks/2. What is a deep net.mp4 6.7 MB
4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp4 6.5 MB
2. Introduction to neural networks/14. Graphical representation.mp4 6.4 MB
15. Conclusion/2. What’s further out there in the machine and deep learning world.mp4 6.3 MB
11. Preprocessing/5. One-hot and binary encoding.mp4 6.2 MB
10. Gradient descent and learning rates/3. Momentum.mp4 6.1 MB
11. Preprocessing/4. Dealing with categorical data.mp4 6.1 MB
3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp4 6.0 MB
9. Initialization/3. Xavier initialization.mp4 5.8 MB
2. Introduction to neural networks/16. The objective function.mp4 5.7 MB
9. Initialization/2. Types of simple initializations.mp4 5.6 MB
15. Conclusion/5. An overview of RNNs.mp4 4.9 MB
6. Going deeper Introduction to deep neural networks/1. Layers.mp4 4.7 MB
10. Gradient descent and learning rates/2. Gradient descent pitfalls.mp4 4.3 MB
11. Preprocessing/2. Basic preprocessing.mp4 3.7 MB
10. Gradient descent and learning rates/5. Learning rate schedules. A picture.mp4 3.1 MB
6. Going deeper Introduction to deep neural networks/1.1 Course Notes - Section 6.pdf 936.4 KB
6. Going deeper Introduction to deep neural networks/2.1 Course Notes - Section 6.pdf 936.4 KB
2. Introduction to neural networks/1.1 Course Notes - Section 2.pdf 927.7 KB
2. Introduction to neural networks/10.1 Course Notes - Section 2.pdf 927.7 KB
2. Introduction to neural networks/12.1 Course Notes - Section 2.pdf 927.7 KB
2. Introduction to neural networks/14.1 Course Notes - Section 2.pdf 927.7 KB
2. Introduction to neural networks/16.1 Course Notes - Section 2.pdf 927.7 KB
2. Introduction to neural networks/18.1 Course Notes - Section 2.pdf 927.7 KB
2. Introduction to neural networks/20.1 Course Notes - Section 2.pdf 927.7 KB
2. Introduction to neural networks/22.1 Course Notes - Section 2.pdf 927.7 KB
2. Introduction to neural networks/24.1 Course Notes - Section 2.pdf 927.7 KB
2. Introduction to neural networks/3.1 Course Notes - Section 2.pdf 927.7 KB
2. Introduction to neural networks/5.1 Course Notes - Section 2.pdf 927.7 KB
2. Introduction to neural networks/7.1 Course Notes - Section 2.pdf 927.7 KB
13. Business case/1.1 Audiobooks_data.csv 625.2 KB
13. Business case/4.1 Audiobooks_data.csv 625.2 KB
13. Business case/5.2 Audiobooks_data.csv 625.2 KB
3. Setting up the working environment/7.1 Shortcuts for Jupyter.pdf 619.2 KB
7. Backpropagation. A peek into the Mathematics of Optimization/1.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 182.4 KB
2. Introduction to neural networks/22.2 GD-function-example.xlsx 42.3 KB
13. Business case/4. Preprocessing the data.srt 12.3 KB
14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.srt 11.8 KB
4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.srt 10.9 KB
13. Business case/1. Exploring the dataset and identifying predictors.srt 10.7 KB
1. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.srt 10.1 KB
14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.srt 9.5 KB
12. The MNIST example/6. Preprocess the data - shuffle and batch the data.srt 9.3 KB
2. Introduction to neural networks/22. One parameter gradient descent.srt 8.5 KB
12. The MNIST example/10. Learning.srt 7.9 KB
5. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.srt 7.8 KB
13. Business case/9. Setting an early stopping mechanism.srt 7.8 KB
2. Introduction to neural networks/24. N-parameter gradient descent.srt 7.5 KB
12. The MNIST example/8. Outline the model.srt 7.2 KB
8. Overfitting/6. Early stopping.srt 6.9 KB
4. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.srt 6.8 KB
3. Setting up the working environment/6. The Jupyter dashboard - part 2.srt 6.8 KB
6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.srt 6.7 KB
15. Conclusion/3. An overview of CNNs.srt 6.5 KB
3. Setting up the working environment/9. Installing TensorFlow 2.srt 6.4 KB
3. Setting up the working environment/2. Why Python and why Jupyter.srt 6.3 KB
12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.srt 6.3 KB
13. Business case/8. Learning and interpreting the result.srt 6.3 KB
1. Welcome! Course introduction/2. What does the course cover.srt 6.2 KB
5. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.srt 6.2 KB
14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.srt 6.1 KB
12. The MNIST example/13. Testing the model.srt 6.0 KB
10. Gradient descent and learning rates/4. Learning rate schedules.srt 6.0 KB
11. Preprocessing/3. Standardization.srt 6.0 KB
2. Introduction to neural networks/1. Introduction to neural networks.srt 5.9 KB
8. Overfitting/1. Underfitting and overfitting.srt 5.6 KB
2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.srt 5.5 KB
14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.srt 5.4 KB
2. Introduction to neural networks/20. Cross-entropy loss.srt 5.3 KB
2. Introduction to neural networks/5. Types of machine learning.srt 5.3 KB
5. TensorFlow - An introduction/1. TensorFlow outline.srt 5.2 KB
10. Gradient descent and learning rates/6. Adaptive learning rate schedules.srt 5.2 KB
15. Conclusion/1. See how much you have learned.srt 5.2 KB
6. Going deeper Introduction to deep neural networks/5. Activation functions.srt 5.2 KB
15. Conclusion/6. An overview of non-NN approaches.srt 5.2 KB
10. Gradient descent and learning rates/1. Stochastic gradient descent.srt 4.9 KB
8. Overfitting/3. Training and validation.srt 4.9 KB
11. Preprocessing/5. One-hot and binary encoding.srt 4.8 KB
13. Business case/6. Load the preprocessed data.srt 4.7 KB
3. Setting up the working environment/4. Installing Anaconda.srt 4.6 KB
4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.srt 4.5 KB
13. Business case/3. Balancing the dataset.srt 4.5 KB
4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.srt 4.4 KB
6. Going deeper Introduction to deep neural networks/7. Backpropagation.srt 4.4 KB
14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.srt 4.3 KB
6. Going deeper Introduction to deep neural networks/6. Softmax activation.srt 4.3 KB
2. Introduction to neural networks/3. Training the model.srt 4.3 KB
14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.srt 4.3 KB
8. Overfitting/5. N-fold cross validation.srt 4.2 KB
5. TensorFlow - An introduction/7. Cutomizing your model.srt 4.1 KB
14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.srt 4.1 KB
14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.srt 4.0 KB
6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.srt 4.0 KB
2. Introduction to neural networks/7. The linear model.srt 3.9 KB
11. Preprocessing/1. Preprocessing introduction.srt 3.9 KB
6. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.srt 3.8 KB
14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.srt 3.8 KB
9. Initialization/3. Xavier initialization.srt 3.7 KB
9. Initialization/2. Types of simple initializations.srt 3.7 KB
5. TensorFlow - An introduction/2. TensorFlow 2 intro.srt 3.6 KB
15. Conclusion/5. An overview of RNNs.srt 3.6 KB
14. Appendix Linear Algebra Fundamentals/5. Tensors.srt 3.6 KB
12. The MNIST example/1. The dataset.srt 3.6 KB
8. Overfitting/4. Training, validation, and test.srt 3.6 KB
9. Initialization/1. Initialization - Introduction.srt 3.5 KB
10. Gradient descent and learning rates/3. Momentum.srt 3.5 KB
12. The MNIST example/2. How to tackle the MNIST.srt 3.5 KB
5. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.srt 3.5 KB
10. Gradient descent and learning rates/7. Adaptive moment estimation.srt 3.3 KB
6. Going deeper Introduction to deep neural networks/2. What is a deep net.srt 3.3 KB
3. Setting up the working environment/5. The Jupyter dashboard - part 1.srt 3.1 KB
2. Introduction to neural networks/10. The linear model. Multiple inputs.srt 3.1 KB
12. The MNIST example/3. Importing the relevant packages and load the data.srt 3.1 KB
12. The MNIST example/9. Select the loss and the optimizer.srt 3.0 KB
10. Gradient descent and learning rates/2. Gradient descent pitfalls.srt 2.8 KB
2. Introduction to neural networks/18. L2-norm loss.srt 2.8 KB
11. Preprocessing/4. Dealing with categorical data.srt 2.8 KB
8. Overfitting/2. Underfitting and overfitting - classification.srt 2.7 KB
2. Introduction to neural networks/14. Graphical representation.srt 2.7 KB
14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.srt 2.6 KB
15. Conclusion/2. What’s further out there in the machine and deep learning world.srt 2.5 KB
16. Bonus lecture/1. Bonus lecture Next steps.html 2.5 KB
6. Going deeper Introduction to deep neural networks/1. Layers.srt 2.4 KB
10. Gradient descent and learning rates/5. Learning rate schedules. A picture.srt 2.2 KB
12. The MNIST example/12. MNIST - solutions.html 2.1 KB
13. Business case/11. Testing the model.srt 2.0 KB
2. Introduction to neural networks/16. The objective function.srt 2.0 KB
13. Business case/2. Outlining the business case solution.srt 2.0 KB
12. The MNIST example/11. MNIST - exercises.html 2.0 KB
11. Preprocessing/2. Basic preprocessing.srt 1.7 KB
4. Minimal example - your first machine learning algorithm/5. Minimal example - Exercises.html 1.6 KB
3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.srt 1.4 KB
5. TensorFlow - An introduction/3. A Note on Coding in TensorFlow.srt 1.4 KB
15. Conclusion/4. How DeepMind uses deep learning.html 1.4 KB
5. TensorFlow - An introduction/8. Minimal example with TensorFlow - Exercises.html 1.4 KB
2. Introduction to neural networks/9. Need Help with Linear Algebra.html 829 bytes
1. Welcome! Course introduction/4. Download All Resources and Important FAQ.html 720 bytes
7. Backpropagation. A peek into the Mathematics of Optimization/1. Backpropagation. A peek into the Mathematics of Optimization.html 539 bytes
13. Business case/12. Final exercise.html 445 bytes
13. Business case/5. Preprocessing exercise.html 404 bytes
3. Setting up the working environment/7. Jupyter Shortcuts.html 332 bytes
3. Setting up the working environment/11. Installing packages - solution.html 267 bytes
14. Appendix Linear Algebra Fundamentals/7.1 Errors when Adding Matrices Python Notebook.html 220 bytes
3. Setting up the working environment/10. Installing packages - exercise.html 198 bytes
13. Business case/10. Setting an early stopping mechanism - Exercise.html 191 bytes
14. Appendix Linear Algebra Fundamentals/4.1 Scalars, Vectors and Matrices Python Notebook.html 181 bytes
14. Appendix Linear Algebra Fundamentals/6.1 Addition and Subtraction Python Notebook.html 178 bytes
12. The MNIST example/12.3 5. TensorFlow MNIST - Exercise 5 Solution.html 172 bytes
12. The MNIST example/12.8 4. TensorFlow MNIST - Exercise 4 Solution.html 172 bytes
13. Business case/7.1 TensorFlow Business Case - Machine Learning - Part 1.html 172 bytes
13. Business case/8.1 TensorFlow Business Case - Machine Learning - Part 2.html 172 bytes
13. Business case/9.1 TensorFlow Business Case - Machine Learning - Part 3.html 172 bytes
14. Appendix Linear Algebra Fundamentals/10.1 Dot Product of Matrices Python Notebook.html 171 bytes
1. Welcome! Course introduction/3. What does the course cover - Quiz.html 168 bytes
2. Introduction to neural networks/11. The linear model. Multiple inputs - Quiz.html 168 bytes
2. Introduction to neural networks/13. The linear model. Multiple inputs and multiple outputs - Quiz.html 168 bytes
2. Introduction to neural networks/15. Graphical representation - Quiz.html 168 bytes
2. Introduction to neural networks/17. The objective function - Quiz.html 168 bytes
2. Introduction to neural networks/19. L2-norm loss - Quiz.html 168 bytes
2. Introduction to neural networks/2. Introduction to neural networks - Quiz.html 168 bytes
2. Introduction to neural networks/21. Cross-entropy loss - Quiz.html 168 bytes
2. Introduction to neural networks/23. One parameter gradient descent - Quiz.html 168 bytes
2. Introduction to neural networks/25. N-parameter gradient descent - Quiz.html 168 bytes
2. Introduction to neural networks/4. Training the model - Quiz.html 168 bytes
2. Introduction to neural networks/6. Types of machine learning - Quiz.html 168 bytes
2. Introduction to neural networks/8. The linear model - Quiz.html 168 bytes
3. Setting up the working environment/3. Why Python and why Jupyter - Quiz.html 168 bytes
3. Setting up the working environment/8. The Jupyter dashboard - Quiz.html 168 bytes
13. Business case/5.3 TensorFlow Business Case - Preprocessing Exercise Solution.html 167 bytes
14. Appendix Linear Algebra Fundamentals/8.1 Transpose of a Matrix Python Notebook.html 167 bytes
13. Business case/11.1 TensorFlow Business Case - Machine Learning Complete Code with Comments.html 166 bytes
13. Business case/12.1 TensorFlow Business Case - Machine Learning Complete Code with Comments.html 166 bytes
12. The MNIST example/12.1 8. TensorFlow MNIST - Exercise 8 Solution.html 165 bytes
12. The MNIST example/12.4 9. TensorFlow MNIST - Exercise 9 Solution.html 165 bytes
13. Business case/4.2 TensorFlow Business Case - Preprocessing with Comments.html 163 bytes
5. TensorFlow - An introduction/7.2 TensorFlow Minimal Example - Complete Code with Comments.html 163 bytes
12. The MNIST example/12.5 6. TensorFlow MNIST - Exercise 6 Solution.html 162 bytes
12. The MNIST example/12.6 7. TensorFlow MNIST - Exercise 7 Solution.html 162 bytes
5. TensorFlow - An introduction/8.2 TensorFlow Minimal Example - Exercise 2_1 - Solution.html 162 bytes
5. TensorFlow - An introduction/8.5 TensorFlow Minimal Example - Exercise 2_2 - Solution.html 162 bytes
12. The MNIST example/12.7 3. TensorFlow MNIST - Exercise 3 Solution.html 160 bytes
5. TensorFlow - An introduction/8.1 TensorFlow Minimal Example - Exercise 1 - Solution.html 160 bytes
5. TensorFlow - An introduction/8.3 TensorFlow Minimal Example - Exercise 3 - Solution.html 160 bytes
13. Business case/5.1 TensorFlow Business Case - Preprocessing Exercise.html 158 bytes
12. The MNIST example/12.10 10. TensorFlow MNIST - Exercise 10 Solution.html 157 bytes
14. Appendix Linear Algebra Fundamentals/9.1 Dot Product Python Notebook.html 154 bytes
4. Minimal example - your first machine learning algorithm/5.1 Minimal_example_Exercise_3.b. Solution.html 154 bytes
4. Minimal example - your first machine learning algorithm/5.6 Minimal_example_Exercise_3.d. Solution.html 154 bytes
4. Minimal example - your first machine learning algorithm/5.7 Minimal_example_Exercise_3.a. Solution.html 154 bytes
4. Minimal example - your first machine learning algorithm/5.9 Minimal_example_Exercise_3.c. Solution.html 154 bytes
5. TensorFlow - An introduction/8.4 TensorFlow Minimal Example - All Exercises.html 154 bytes
12. The MNIST example/13.1 TensorFlow MNIST - Complete Code with Comments.html 153 bytes
12. The MNIST example/10.1 TensorFlow MNIST - Part 6 with comments.html 150 bytes
12. The MNIST example/12.2 1. TensorFlow MNIST - Exercise 1 Solution.html 150 bytes
12. The MNIST example/12.9 2. TensorFlow MNIST - Exercise 2 Solution.html 150 bytes
12. The MNIST example/3.1 TensorFlow MNIST - Part 1 with comments.html 150 bytes
12. The MNIST example/5.1 TensorFlow MNIST - Part 2 with comments.html 150 bytes
12. The MNIST example/7.1 TensorFlow MNIST - Part 3 with comments.html 150 bytes
12. The MNIST example/8.1 TensorFlow MNIST - Part 4 with comments.html 150 bytes
12. The MNIST example/9.1 TensorFlow MNIST - Part 5 with comments.html 150 bytes
13. Business case/4.3 TensorFlow Business Case - Preprocessing.html 149 bytes
4. Minimal example - your first machine learning algorithm/5.10 Minimal_example_Exercise_6_Solution.html 149 bytes
4. Minimal example - your first machine learning algorithm/5.2 Minimal_example_Exercise_5_Solution.html 149 bytes
4. Minimal example - your first machine learning algorithm/5.3 Minimal_example_Exercise_1_Solution.html 149 bytes
4. Minimal example - your first machine learning algorithm/5.4 Minimal_example_Exercise_4_Solution.html 149 bytes
4. Minimal example - your first machine learning algorithm/5.8 Minimal_example_Exercise_2_Solution.html 149 bytes
5. TensorFlow - An introduction/7.1 TensorFlow Minimal Example - Complete Code.html 149 bytes
14. Appendix Linear Algebra Fundamentals/5.1 Tensors Notebook.html 148 bytes
5. TensorFlow - An introduction/4.1 TensorFlow Minimal Example - Part 1.html 146 bytes
5. TensorFlow - An introduction/5.1 TensorFlow Minimal Example - Part 2.html 146 bytes
5. TensorFlow - An introduction/6.1 TensorFlow Minimal Example - Part 3.html 146 bytes
4. Minimal example - your first machine learning algorithm/4.1 Minimal example - part 4.html 145 bytes
12. The MNIST example/11.1 TensorFlow MNIST - All Exercises.html 144 bytes
4. Minimal example - your first machine learning algorithm/5.5 Minimal_example_All_Exercises.html 143 bytes
12. The MNIST example/13.2 TensorFlow MNIST - Complete Code.html 139 bytes
4. Minimal example - your first machine learning algorithm/1.1 Minimal example Part 1.html 136 bytes
4. Minimal example - your first machine learning algorithm/2.1 Minimal example - part 2.html 136 bytes
4. Minimal example - your first machine learning algorithm/3.1 Minimal example - part 3.html 136 bytes
12. The MNIST example/5. Preprocess the data - scale the test data.html 81 bytes
12. The MNIST example/7. Preprocess the data - shuffle and batch the data.html 81 bytes
13. Business case/7. Load the preprocessed data - Exercise.html 79 bytes
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[DesireCourse.Net] Udemy - Deep Learning with TensorFlow 2.0 [2020]

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