[Tutorialsplanet.NET] Udemy - Deep Learning with TensorFlow 2.0 [2020]

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[Tutorialsplanet.NET] Udemy - Deep Learning with TensorFlow 2.0 [2020]

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文件列表

  • 14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4 151.3 MB
  • 1. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.mp4 110.9 MB
  • 13. Business case/4. Preprocessing the data.mp4 88.4 MB
  • 13. Business case/1. Exploring the dataset and identifying predictors.mp4 69.5 MB
  • 13. Business case/9. Setting an early stopping mechanism.mp4 52.2 MB
  • 14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.mp4 52.2 MB
  • 14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.mp4 51.8 MB
  • 12. The MNIST example/6. Preprocess the data - shuffle and batch the data.mp4 43.6 MB
  • 12. The MNIST example/10. Learning.mp4 42.9 MB
  • 2. Introduction to neural networks/24. N-parameter gradient descent.mp4 41.4 MB
  • 3. Setting up the working environment/9. Installing TensorFlow 2.mp4 40.6 MB
  • 2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp4 40.1 MB
  • 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.mp4 39.9 MB
  • 5. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 36.4 MB
  • 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.mp4 35.5 MB
  • 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp4 35.2 MB
  • 5. TensorFlow - An introduction/1. TensorFlow outline.mp4 35.2 MB
  • 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp4 34.2 MB
  • 3. Setting up the working environment/2. Why Python and why Jupyter.mp4 33.6 MB
  • 13. Business case/8. Learning and interpreting the result.mp4 32.7 MB
  • 13. Business case/3. Balancing the dataset.mp4 31.9 MB
  • 5. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.mp4 31.7 MB
  • 12. The MNIST example/13. Testing the model.mp4 31.0 MB
  • 12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.mp4 30.5 MB
  • 3. Setting up the working environment/4. Installing Anaconda.mp4 29.8 MB
  • 12. The MNIST example/8. Outline the model.mp4 29.6 MB
  • 14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.mp4 28.0 MB
  • 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp4 25.1 MB
  • 5. TensorFlow - An introduction/7. Cutomizing your model.mp4 24.0 MB
  • 14. Appendix Linear Algebra Fundamentals/5. Tensors.mp4 23.6 MB
  • 5. TensorFlow - An introduction/2. TensorFlow 2 intro.mp4 23.1 MB
  • 4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp4 21.8 MB
  • 3. Setting up the working environment/6. The Jupyter dashboard - part 2.mp4 19.7 MB
  • 12. The MNIST example/2. How to tackle the MNIST.mp4 19.6 MB
  • 2. Introduction to neural networks/22. One parameter gradient descent.mp4 18.6 MB
  • 13. Business case/6. Load the preprocessed data.mp4 18.4 MB
  • 5. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.mp4 17.2 MB
  • 1. Welcome! Course introduction/2. What does the course cover.mp4 17.1 MB
  • 12. The MNIST example/3. Importing the relevant packages and load the data.mp4 17.1 MB
  • 15. Conclusion/1. See how much you have learned.mp4 14.6 MB
  • 12. The MNIST example/9. Select the loss and the optimizer.mp4 14.6 MB
  • 2. Introduction to neural networks/1. Introduction to neural networks.mp4 14.2 MB
  • 6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp4 14.0 MB
  • 12. The MNIST example/1. The dataset.mp4 14.0 MB
  • 2. Introduction to neural networks/5. Types of machine learning.mp4 12.8 MB
  • 2. Introduction to neural networks/20. Cross-entropy loss.mp4 11.9 MB
  • 14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.mp4 11.7 MB
  • 8. Overfitting/1. Underfitting and overfitting.mp4 11.6 MB
  • 6. Going deeper Introduction to deep neural networks/7. Backpropagation.mp4 11.6 MB
  • 15. Conclusion/3. An overview of CNNs.mp4 11.5 MB
  • 13. Business case/11. Testing the model.mp4 11.3 MB
  • 4. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.mp4 11.2 MB
  • 10. Gradient descent and learning rates/4. Learning rate schedules.mp4 10.8 MB
  • 4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp4 10.2 MB
  • 8. Overfitting/6. Early stopping.mp4 9.9 MB
  • 10. Gradient descent and learning rates/1. Stochastic gradient descent.mp4 9.8 MB
  • 8. Overfitting/3. Training and validation.mp4 9.7 MB
  • 2. Introduction to neural networks/7. The linear model.mp4 9.6 MB
  • 6. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.mp4 9.4 MB
  • 10. Gradient descent and learning rates/6. Adaptive learning rate schedules.mp4 9.3 MB
  • 2. Introduction to neural networks/3. Training the model.mp4 9.2 MB
  • 6. Going deeper Introduction to deep neural networks/5. Activation functions.mp4 9.2 MB
  • 3. Setting up the working environment/5. The Jupyter dashboard - part 1.mp4 9.1 MB
  • 11. Preprocessing/1. Preprocessing introduction.mp4 8.8 MB
  • 11. Preprocessing/3. Standardization.mp4 8.7 MB
  • 9. Initialization/1. Initialization - Introduction.mp4 8.4 MB
  • 15. Conclusion/6. An overview of non-NN approaches.mp4 8.2 MB
  • 10. Gradient descent and learning rates/7. Adaptive moment estimation.mp4 8.1 MB
  • 2. Introduction to neural networks/10. The linear model. Multiple inputs.mp4 7.9 MB
  • 8. Overfitting/4. Training, validation, and test.mp4 7.8 MB
  • 6. Going deeper Introduction to deep neural networks/6. Softmax activation.mp4 7.7 MB
  • 13. Business case/2. Outlining the business case solution.mp4 7.7 MB
  • 2. Introduction to neural networks/18. L2-norm loss.mp4 7.6 MB
  • 8. Overfitting/5. N-fold cross validation.mp4 7.3 MB
  • 6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.mp4 7.2 MB
  • 8. Overfitting/2. Underfitting and overfitting - classification.mp4 7.1 MB
  • 5. TensorFlow - An introduction/3. A Note on Coding in TensorFlow.mp4 7.1 MB
  • 6. Going deeper Introduction to deep neural networks/2. What is a deep net.mp4 7.1 MB
  • 4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp4 6.9 MB
  • 2. Introduction to neural networks/14. Graphical representation.mp4 6.7 MB
  • 15. Conclusion/2. What’s further out there in the machine and deep learning world.mp4 6.6 MB
  • 11. Preprocessing/5. One-hot and binary encoding.mp4 6.5 MB
  • 10. Gradient descent and learning rates/3. Momentum.mp4 6.4 MB
  • 11. Preprocessing/4. Dealing with categorical data.mp4 6.4 MB
  • 3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp4 6.2 MB
  • 9. Initialization/3. Xavier initialization.mp4 6.1 MB
  • 2. Introduction to neural networks/16. The objective function.mp4 6.0 MB
  • 9. Initialization/2. Types of simple initializations.mp4 5.9 MB
  • 15. Conclusion/5. An overview of RNNs.mp4 5.1 MB
  • 6. Going deeper Introduction to deep neural networks/1. Layers.mp4 5.0 MB
  • 10. Gradient descent and learning rates/2. Gradient descent pitfalls.mp4 4.5 MB
  • 11. Preprocessing/2. Basic preprocessing.mp4 3.8 MB
  • 10. Gradient descent and learning rates/5. Learning rate schedules. A picture.mp4 3.3 MB
  • 6. Going deeper Introduction to deep neural networks/1.1 Course Notes - Section 6.pdf 958.9 kB
  • 6. Going deeper Introduction to deep neural networks/2.1 Course Notes - Section 6.pdf 958.9 kB
  • 2. Introduction to neural networks/1.1 Course Notes - Section 2.pdf 949.9 kB
  • 2. Introduction to neural networks/10.1 Course Notes - Section 2.pdf 949.9 kB
  • 2. Introduction to neural networks/12.1 Course Notes - Section 2.pdf 949.9 kB
  • 2. Introduction to neural networks/14.1 Course Notes - Section 2.pdf 949.9 kB
  • 2. Introduction to neural networks/16.1 Course Notes - Section 2.pdf 949.9 kB
  • 2. Introduction to neural networks/18.1 Course Notes - Section 2.pdf 949.9 kB
  • 2. Introduction to neural networks/20.1 Course Notes - Section 2.pdf 949.9 kB
  • 2. Introduction to neural networks/22.1 Course Notes - Section 2.pdf 949.9 kB
  • 2. Introduction to neural networks/24.1 Course Notes - Section 2.pdf 949.9 kB
  • 2. Introduction to neural networks/3.1 Course Notes - Section 2.pdf 949.9 kB
  • 2. Introduction to neural networks/5.1 Course Notes - Section 2.pdf 949.9 kB
  • 2. Introduction to neural networks/7.1 Course Notes - Section 2.pdf 949.9 kB
  • 13. Business case/1.1 Audiobooks_data.csv 640.2 kB
  • 13. Business case/4.3 Audiobooks_data.csv 640.2 kB
  • 13. Business case/5.3 Audiobooks_data.csv 640.2 kB
  • 3. Setting up the working environment/7.1 Shortcuts for Jupyter.pdf 634.0 kB
  • 7. Backpropagation. A peek into the Mathematics of Optimization/1.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 186.8 kB
  • 2. Introduction to neural networks/22.2 GD-function-example.xlsx 43.4 kB
  • 13. Business case/4. Preprocessing the data.srt 12.6 kB
  • 14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.srt 12.1 kB
  • 4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.srt 11.1 kB
  • 13. Business case/1. Exploring the dataset and identifying predictors.srt 10.9 kB
  • 1. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.srt 10.4 kB
  • 14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.srt 9.7 kB
  • 12. The MNIST example/6. Preprocess the data - shuffle and batch the data.srt 9.5 kB
  • 2. Introduction to neural networks/22. One parameter gradient descent.srt 8.7 kB
  • 12. The MNIST example/10. Learning.srt 8.1 kB
  • 5. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.srt 8.0 kB
  • 13. Business case/9. Setting an early stopping mechanism.srt 8.0 kB
  • 2. Introduction to neural networks/24. N-parameter gradient descent.srt 7.7 kB
  • 12. The MNIST example/8. Outline the model.srt 7.4 kB
  • 8. Overfitting/6. Early stopping.srt 7.0 kB
  • 4. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.srt 7.0 kB
  • 3. Setting up the working environment/6. The Jupyter dashboard - part 2.srt 6.9 kB
  • 6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.srt 6.8 kB
  • 15. Conclusion/3. An overview of CNNs.srt 6.6 kB
  • 3. Setting up the working environment/9. Installing TensorFlow 2.srt 6.5 kB
  • 3. Setting up the working environment/2. Why Python and why Jupyter.srt 6.5 kB
  • 12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.srt 6.4 kB
  • 13. Business case/8. Learning and interpreting the result.srt 6.4 kB
  • 1. Welcome! Course introduction/2. What does the course cover.srt 6.4 kB
  • 5. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.srt 6.3 kB
  • 14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.srt 6.3 kB
  • 12. The MNIST example/13. Testing the model.srt 6.2 kB
  • 10. Gradient descent and learning rates/4. Learning rate schedules.srt 6.1 kB
  • 11. Preprocessing/3. Standardization.srt 6.1 kB
  • 2. Introduction to neural networks/1. Introduction to neural networks.srt 6.1 kB
  • 8. Overfitting/1. Underfitting and overfitting.srt 5.8 kB
  • 2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.srt 5.6 kB
  • 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.srt 5.5 kB
  • 2. Introduction to neural networks/20. Cross-entropy loss.srt 5.5 kB
  • 2. Introduction to neural networks/5. Types of machine learning.srt 5.4 kB
  • 5. TensorFlow - An introduction/1. TensorFlow outline.srt 5.4 kB
  • 10. Gradient descent and learning rates/6. Adaptive learning rate schedules.srt 5.3 kB
  • 15. Conclusion/1. See how much you have learned.srt 5.3 kB
  • 6. Going deeper Introduction to deep neural networks/5. Activation functions.srt 5.3 kB
  • 15. Conclusion/6. An overview of non-NN approaches.srt 5.3 kB
  • 10. Gradient descent and learning rates/1. Stochastic gradient descent.srt 5.0 kB
  • 8. Overfitting/3. Training and validation.srt 5.0 kB
  • 11. Preprocessing/5. One-hot and binary encoding.srt 4.9 kB
  • 13. Business case/6. Load the preprocessed data.srt 4.8 kB
  • 3. Setting up the working environment/4. Installing Anaconda.srt 4.7 kB
  • 4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.srt 4.6 kB
  • 13. Business case/3. Balancing the dataset.srt 4.6 kB
  • 4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.srt 4.5 kB
  • 6. Going deeper Introduction to deep neural networks/7. Backpropagation.srt 4.5 kB
  • 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.srt 4.4 kB
  • 6. Going deeper Introduction to deep neural networks/6. Softmax activation.srt 4.4 kB
  • 2. Introduction to neural networks/3. Training the model.srt 4.4 kB
  • 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.srt 4.4 kB
  • 8. Overfitting/5. N-fold cross validation.srt 4.3 kB
  • 5. TensorFlow - An introduction/7. Cutomizing your model.srt 4.2 kB
  • 14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.srt 4.2 kB
  • 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.srt 4.1 kB
  • 6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.srt 4.1 kB
  • 2. Introduction to neural networks/7. The linear model.srt 4.0 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 3.9 kB
  • 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.srt 3.9 kB
  • 9. Initialization/3. Xavier initialization.srt 3.8 kB
  • 9. Initialization/2. Types of simple initializations.srt 3.8 kB
  • 5. TensorFlow - An introduction/2. TensorFlow 2 intro.srt 3.7 kB
  • 15. Conclusion/5. An overview of RNNs.srt 3.7 kB
  • 14. Appendix Linear Algebra Fundamentals/5. Tensors.srt 3.7 kB
  • 12. The MNIST example/1. The dataset.srt 3.7 kB
  • 8. Overfitting/4. Training, validation, and test.srt 3.6 kB
  • 9. Initialization/1. Initialization - Introduction.srt 3.6 kB
  • 10. Gradient descent and learning rates/3. Momentum.srt 3.6 kB
  • 12. The MNIST example/2. How to tackle the MNIST.srt 3.6 kB
  • 5. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.srt 3.6 kB
  • 10. Gradient descent and learning rates/7. Adaptive moment estimation.srt 3.4 kB
  • 6. Going deeper Introduction to deep neural networks/2. What is a deep net.srt 3.4 kB
  • 3. Setting up the working environment/5. The Jupyter dashboard - part 1.srt 3.2 kB
  • 2. Introduction to neural networks/10. The linear model. Multiple inputs.srt 3.2 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 3.1 kB
  • 10. Gradient descent and learning rates/2. Gradient descent pitfalls.srt 2.9 kB
  • 2. Introduction to neural networks/18. L2-norm loss.srt 2.9 kB
  • 11. Preprocessing/4. Dealing with categorical data.srt 2.8 kB
  • 8. Overfitting/2. Underfitting and overfitting - classification.srt 2.8 kB
  • 2. Introduction to neural networks/14. Graphical representation.srt 2.8 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 2.6 kB
  • 16. Bonus lecture/1. Bonus lecture Next steps.html 2.6 kB
  • 6. Going deeper Introduction to deep neural networks/1. Layers.srt 2.5 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 2.2 kB
  • 13. Business case/11. Testing the model.srt 2.1 kB
  • 2. Introduction to neural networks/16. The objective function.srt 2.1 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/12.4 4. TensorFlow MNIST - Exercise 4 Solution.html 172 Bytes
  • 12. The MNIST example/12.5 5. TensorFlow MNIST - Exercise 5 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/5.2 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/12.6 8. TensorFlow MNIST - Exercise 8 Solution.html 165 Bytes
  • 12. The MNIST example/12.8 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/7.1 TensorFlow Minimal Example - Complete Code with Comments.html 163 Bytes
  • 12. The MNIST example/12.10 7. TensorFlow MNIST - Exercise 7 Solution.html 162 Bytes
  • 12. The MNIST example/12.9 6. TensorFlow MNIST - Exercise 6 Solution.html 162 Bytes
  • 5. TensorFlow - An introduction/8.1 TensorFlow Minimal Example - Exercise 2_2 - Solution.html 162 Bytes
  • 5. TensorFlow - An introduction/8.4 TensorFlow Minimal Example - Exercise 2_1 - Solution.html 162 Bytes
  • 12. The MNIST example/12.1 3. TensorFlow MNIST - Exercise 3 Solution.html 160 Bytes
  • 5. TensorFlow - An introduction/8.2 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/12.7 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/5.2 Minimal_example_Exercise_3.c. Solution.html 154 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.4 Minimal_example_Exercise_3.a. Solution.html 154 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.7 Minimal_example_Exercise_3.d. Solution.html 154 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.8 Minimal_example_Exercise_3.b. Solution.html 154 Bytes
  • 5. TensorFlow - An introduction/8.5 TensorFlow Minimal Example - All Exercises.html 154 Bytes
  • 12. The MNIST example/13.2 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/12.3 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/4.1 TensorFlow Business Case - Preprocessing.html 149 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.1 Minimal_example_Exercise_6_Solution.html 149 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.3 Minimal_example_Exercise_4_Solution.html 149 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.5 Minimal_example_Exercise_5_Solution.html 149 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.6 Minimal_example_Exercise_1_Solution.html 149 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.9 Minimal_example_Exercise_2_Solution.html 149 Bytes
  • 5. TensorFlow - An introduction/7.2 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/5.10 Minimal_example_All_Exercises.html 143 Bytes
  • 12. The MNIST example/13.1 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
  • [Tutorialsplanet.NET].url 128 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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