[FreeCourseSite.com] Udemy - Master Deep Learning with TensorFlow in Python

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[FreeCourseSite.com] Udemy - Master Deep Learning with TensorFlow in Python

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收录时间：2021-03-10
最近下载：2024-11-06

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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
  • 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
  • 2. Introduction to neural networks/24. N-parameter gradient descent.mp4 41.4 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
  • 13. Business case/4. Preprocessing the data.mp4 36.0 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
  • 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp4 34.2 MB
  • 13. Business case/6. Create a class for batching.mp4 29.0 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 25.2 MB
  • 13. Business case/1. Exploring the dataset and identifying predictors.mp4 24.4 MB
  • 14. Appendix Linear Algebra Fundamentals/5. Tensors.mp4 23.6 MB
  • 12. The MNIST example/9. Discuss the results and test.mp4 23.0 MB
  • 4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp4 21.8 MB
  • 13. Business case/7. Outlining the model.mp4 20.4 MB
  • 12. The MNIST example/4. Outlining the model.mp4 19.3 MB
  • 2. Introduction to neural networks/22. One parameter gradient descent.mp4 18.6 MB
  • 1. Welcome! Course introduction/2. What does the course cover.mp4 17.2 MB
  • 12. The MNIST example/8. Learning.mp4 16.7 MB
  • 5. TensorFlow - An introduction/1. TensorFlow outline.mp4 15.2 MB
  • 5. TensorFlow - An introduction/6. Model output.mp4 15.0 MB
  • 15. Conclusion/1. See how much you have learned.mp4 14.6 MB
  • 13. Business case/3. Balancing the dataset.mp4 14.5 MB
  • 3. Setting up the working environment/2. Why Python and why Jupyter.mp4 14.3 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 14.1 MB
  • 13. Business case/11. A comment on the homework.mp4 13.6 MB
  • 5. TensorFlow - An introduction/4. Inputs, outputs, targets, weights, biases - model layout.mp4 13.6 MB
  • 12. The MNIST example/6. Accuracy of prediction.mp4 13.0 MB
  • 13. Business case/8. Optimizing the algorithm.mp4 12.8 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
  • 6. Going deeper Introduction to deep neural networks/7. Backpropagation.mp4 11.6 MB
  • 8. Overfitting/1. Underfitting and overfitting.mp4 11.6 MB
  • 15. Conclusion/3. An overview of CNNs.mp4 11.5 MB
  • 3. Setting up the working environment/6. The Jupyter dashboard - part 2.mp4 11.4 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
  • 5. TensorFlow - An introduction/5. Loss function and gradient descent - introducing optimizers.mp4 10.2 MB
  • 8. Overfitting/6. Early stopping.mp4 9.9 MB
  • 3. Setting up the working environment/4. Installing Anaconda.mp4 9.8 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
  • 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 8.2 MB
  • 5. TensorFlow - An introduction/2. TensorFlow intro.mp4 7.9 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.vtt 7.7 MB
  • 6. Going deeper Introduction to deep neural networks/6. Softmax activation.mp4 7.7 MB
  • 12. The MNIST example/1. The dataset.mp4 7.7 MB
  • 12. The MNIST example/2. How to tackle the MNIST.mp4 7.7 MB
  • 2. Introduction to neural networks/18. L2-norm loss.mp4 7.6 MB
  • 12. The MNIST example/5. Declaring the loss and the optimization algorithm.mp4 7.5 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
  • 6. Going deeper Introduction to deep neural networks/2. What is a deep net.mp4 7.0 MB
  • 4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp4 6.9 MB
  • 6. Going deeper Introduction to deep neural networks/7. Backpropagation.vtt 6.8 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
  • 5. TensorFlow - An introduction/3. Types of file formats in TensorFlow.mp4 6.1 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
  • 3. Setting up the working environment/5. The Jupyter dashboard - part 1.mp4 5.9 MB
  • 12. The MNIST example/3. Importing the relevant packages.mp4 5.7 MB
  • 13. Business case/9. Interpreting the result.mp4 5.6 MB
  • 15. Conclusion/5. An overview of RNNs.mp4 5.1 MB
  • 3. Setting up the working environment/9. Installing the TensorFlow package.mp4 5.1 MB
  • 6. Going deeper Introduction to deep neural networks/1. Layers.mp4 5.0 MB
  • 12. The MNIST example/7. Batching and early stopping.mp4 4.8 MB
  • 10. Gradient descent and learning rates/2. Gradient descent pitfalls.mp4 4.5 MB
  • 13. Business case/10. Testing the model.mp4 4.5 MB
  • 13. Business case/2. Outlining the business case solution.mp4 4.0 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
  • 3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp4 2.8 MB
  • 6. Going deeper Introduction to deep neural networks/1.1 Course Notes - Section 6.pdf.pdf 958.9 kB
  • 6. Going deeper Introduction to deep neural networks/2.1 Course Notes - Section 6.pdf.pdf 958.9 kB
  • 2. Introduction to neural networks/1.1 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 2. Introduction to neural networks/10.1 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 2. Introduction to neural networks/12.1 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 2. Introduction to neural networks/14.1 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 2. Introduction to neural networks/16.1 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 2. Introduction to neural networks/18.1 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 2. Introduction to neural networks/20.1 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 2. Introduction to neural networks/22.2 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 2. Introduction to neural networks/24.1 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 2. Introduction to neural networks/3.1 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 2. Introduction to neural networks/5.1 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 2. Introduction to neural networks/7.1 Course Notes - Section 2.pdf.pdf 949.9 kB
  • 13. Business case/1.1 Audiobooks_data.csv.csv 727.8 kB
  • 3. Setting up the working environment/7.1 Shortcuts for Jupyter.pdf.pdf 634.0 kB
  • 7. Backpropagation. A peek into the Mathematics of Optimization/1.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf 186.7 kB
  • 2. Introduction to neural networks/22.1 GD-function-example.xlsx.xlsx 43.4 kB
  • 13. Business case/4. Preprocessing the data.vtt 12.0 kB
  • 14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.vtt 10.6 kB
  • 4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.vtt 9.7 kB
  • 13. Business case/1. Exploring the dataset and identifying predictors.vtt 9.7 kB
  • 12. The MNIST example/8. Learning.vtt 9.1 kB
  • 1. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.vtt 9.0 kB
  • 14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.vtt 8.4 kB
  • 12. The MNIST example/4. Outlining the model.vtt 8.0 kB
  • 2. Introduction to neural networks/22. One parameter gradient descent.vtt 7.6 kB
  • 12. The MNIST example/9. Discuss the results and test.vtt 7.4 kB
  • 5. TensorFlow - An introduction/6. Model output.vtt 7.0 kB
  • 13. Business case/6. Create a class for batching.vtt 7.0 kB
  • 2. Introduction to neural networks/24. N-parameter gradient descent.vtt 6.8 kB
  • 5. TensorFlow - An introduction/4. Inputs, outputs, targets, weights, biases - model layout.vtt 6.6 kB
  • 13. Business case/7. Outlining the model.vtt 6.2 kB
  • 8. Overfitting/6. Early stopping.vtt 6.2 kB
  • 3. Setting up the working environment/6. The Jupyter dashboard - part 2.vtt 6.1 kB
  • 4. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.vtt 6.1 kB
  • 6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.vtt 6.0 kB
  • 13. Business case/8. Optimizing the algorithm.vtt 5.9 kB
  • 15. Conclusion/3. An overview of CNNs.vtt 5.8 kB
  • 3. Setting up the working environment/2. Why Python and why Jupyter.vtt 5.7 kB
  • 1. Welcome! Course introduction/2. What does the course cover.vtt 5.6 kB
  • 14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.vtt 5.4 kB
  • 11. Preprocessing/3. Standardization.vtt 5.4 kB
  • 10. Gradient descent and learning rates/4. Learning rate schedules.vtt 5.4 kB
  • 2. Introduction to neural networks/1. Introduction to neural networks.vtt 5.3 kB
  • 8. Overfitting/1. Underfitting and overfitting.vtt 5.1 kB
  • 2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.vtt 4.9 kB
  • 14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.vtt 4.8 kB
  • 2. Introduction to neural networks/5. Types of machine learning.vtt 4.8 kB
  • 13. Business case/11. A comment on the homework.vtt 4.7 kB
  • 2. Introduction to neural networks/20. Cross-entropy loss.vtt 4.7 kB
  • 15. Conclusion/1. See how much you have learned.vtt 4.7 kB
  • 5. TensorFlow - An introduction/1. TensorFlow outline.vtt 4.7 kB
  • 15. Conclusion/6. An overview of non-NN approaches.vtt 4.7 kB
  • 10. Gradient descent and learning rates/6. Adaptive learning rate schedules.vtt 4.7 kB
  • 12. The MNIST example/6. Accuracy of prediction.vtt 4.7 kB
  • 6. Going deeper Introduction to deep neural networks/5. Activation functions.vtt 4.6 kB
  • 8. Overfitting/3. Training and validation.vtt 4.3 kB
  • 10. Gradient descent and learning rates/1. Stochastic gradient descent.vtt 4.3 kB
  • 5. TensorFlow - An introduction/5. Loss function and gradient descent - introducing optimizers.vtt 4.3 kB
  • 11. Preprocessing/5. One-hot and binary encoding.vtt 4.3 kB
  • 3. Setting up the working environment/4. Installing Anaconda.vtt 4.2 kB
  • 4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.vtt 4.0 kB
  • 4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.vtt 4.0 kB
  • 13. Business case/3. Balancing the dataset.vtt 4.0 kB
  • 2. Introduction to neural networks/3. Training the model.vtt 3.9 kB
  • 14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.vtt 3.9 kB
  • 8. Overfitting/5. N-fold cross validation.vtt 3.8 kB
  • 14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.vtt 3.8 kB
  • 14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.vtt 3.6 kB
  • 14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.vtt 3.6 kB
  • 2. Introduction to neural networks/7. The linear model.vtt 3.6 kB
  • 6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.vtt 3.6 kB
  • 11. Preprocessing/1. Preprocessing introduction.vtt 3.5 kB
  • 6. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.vtt 3.4 kB
  • 14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.vtt 3.4 kB
  • 15. Conclusion/5. An overview of RNNs.vtt 3.3 kB
  • 9. Initialization/3. Xavier initialization.vtt 3.3 kB
  • 9. Initialization/2. Types of simple initializations.vtt 3.3 kB
  • 12. The MNIST example/2. How to tackle the MNIST.vtt 3.3 kB
  • 14. Appendix Linear Algebra Fundamentals/5. Tensors.vtt 3.2 kB
  • 12. The MNIST example/5. Declaring the loss and the optimization algorithm.vtt 3.2 kB
  • 9. Initialization/1. Initialization - Introduction.vtt 3.2 kB
  • 10. Gradient descent and learning rates/3. Momentum.vtt 3.2 kB
  • 8. Overfitting/4. Training, validation, and test.vtt 3.2 kB
  • 12. The MNIST example/1. The dataset.vtt 3.1 kB
  • 5. TensorFlow - An introduction/3. Types of file formats in TensorFlow.vtt 3.1 kB
  • 10. Gradient descent and learning rates/7. Adaptive moment estimation.vtt 3.0 kB
  • 6. Going deeper Introduction to deep neural networks/2. What is a deep net.vtt 2.9 kB
  • 3. Setting up the working environment/9. Installing the TensorFlow package.vtt 2.9 kB
  • 3. Setting up the working environment/5. The Jupyter dashboard - part 1.vtt 2.8 kB
  • 2. Introduction to neural networks/10. The linear model. Multiple inputs.vtt 2.8 kB
  • 13. Business case/9. Interpreting the result.vtt 2.6 kB
  • 16. Bonus lecture/1. Bonus lecture Next steps.html 2.6 kB
  • 10. Gradient descent and learning rates/2. Gradient descent pitfalls.vtt 2.6 kB
  • 12. The MNIST example/7. Batching and early stopping.vtt 2.5 kB
  • 2. Introduction to neural networks/18. L2-norm loss.vtt 2.5 kB
  • 11. Preprocessing/4. Dealing with categorical data.vtt 2.5 kB
  • 8. Overfitting/2. Underfitting and overfitting - classification.vtt 2.4 kB
  • 2. Introduction to neural networks/14. Graphical representation.vtt 2.4 kB
  • 13. Business case/10. Testing the model.vtt 2.4 kB
  • 12. The MNIST example/10. MNIST - exercises.html 2.3 kB
  • 14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.vtt 2.3 kB
  • 15. Conclusion/2. What’s further out there in the machine and deep learning world.vtt 2.3 kB
  • 13. Business case/2. Outlining the business case solution.vtt 2.3 kB
  • 12. The MNIST example/11. MNIST - solutions.html 2.3 kB
  • 6. Going deeper Introduction to deep neural networks/1. Layers.vtt 2.2 kB
  • 12. The MNIST example/3. Importing the relevant packages.vtt 2.0 kB
  • 5. TensorFlow - An introduction/2. TensorFlow intro.vtt 1.9 kB
  • 10. Gradient descent and learning rates/5. Learning rate schedules. A picture.vtt 1.9 kB
  • 2. Introduction to neural networks/16. The objective function.vtt 1.9 kB
  • 5. TensorFlow - An introduction/7. Minimal example - Exercises.html 1.7 kB
  • 4. Minimal example - your first machine learning algorithm/5. Minimal example - Exercises.html 1.6 kB
  • 11. Preprocessing/2. Basic preprocessing.vtt 1.5 kB
  • 15. Conclusion/4. How DeepMind uses deep learning.html 1.4 kB
  • 3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.vtt 1.1 kB
  • 2. Introduction to neural networks/9. Need Help with Linear Algebra.html 829 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 441 Bytes
  • 13. Business case/5. Preprocessing exercise.html 394 Bytes
  • 3. Setting up the working environment/11. Installing packages - solution.html 339 Bytes
  • 3. Setting up the working environment/7. Jupyter Shortcuts.html 332 Bytes
  • 3. Setting up the working environment/10. Installing packages - exercise.html 227 Bytes
  • 14. Appendix Linear Algebra Fundamentals/7.1 Errors when Adding Matrices Python Notebook.html 220 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/11.11 TensorFlow_MNIST_Activation_functions_Part_1_Solution.html 172 Bytes
  • 12. The MNIST example/11.9 MNIST_Activation_functions_Part_2_Solution.html 172 Bytes
  • 14. Appendix Linear Algebra Fundamentals/10.1 Dot Product of Matrices Python Notebook.html 171 Bytes
  • 14. Appendix Linear Algebra Fundamentals/8.1 Transpose of a Matrix Python Notebook.html 167 Bytes
  • 12. The MNIST example/11.10 MNIST_Learning_rate_Part_1_Solution.html 165 Bytes
  • 12. The MNIST example/11.4 MNIST_Learning_rate_Part_2_Solution.html 165 Bytes
  • 12. The MNIST example/11.2 MNIST_take_note_of_time_Solution.html 162 Bytes
  • 12. The MNIST example/11.6 MNIST_Batch_size_Part_2_Solution.html 162 Bytes
  • 12. The MNIST example/11.8 MNIST_Batch_size_Part_1_Solution.html 162 Bytes
  • 5. TensorFlow - An introduction/7.2 TensorFlow_Minimal_Example_Exercise_2_3_Solution.html 162 Bytes
  • 5. TensorFlow - An introduction/7.3 TensorFlow_Minimal_Example_Exercise_2_1_Solution.html 162 Bytes
  • 5. TensorFlow - An introduction/7.6 TensorFlow_Minimal_Example_Exercise_2_2_Solution.html 162 Bytes
  • 5. TensorFlow - An introduction/7.8 TensorFlow_Minimal_Example_Exercise_2_4_Solution.html 162 Bytes
  • 1. Welcome! Course introduction/3. What does the course cover - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/11. The linear model. Multiple inputs - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/13. The linear model. Multiple inputs and multiple outputs - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/15. Graphical representation - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/17. The objective function - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/19. L2-norm loss - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/2. Introduction to neural networks - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/21. Cross-entropy loss - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/23. One parameter gradient descent - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/25. N-parameter gradient descent - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/4. Training the model - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/6. Types of machine learning - Quiz.html 161 Bytes
  • 2. Introduction to neural networks/8. The linear model - Quiz.html 161 Bytes
  • 3. Setting up the working environment/3. Why Python and why Jupyter - Quiz.html 161 Bytes
  • 3. Setting up the working environment/8. The Jupyter dashboard - Quiz.html 161 Bytes
  • 12. The MNIST example/11.3 Width_and_Depth_Solution.html 160 Bytes
  • 5. TensorFlow - An introduction/7.1 TensorFlow_Minimal_Example_Exercise_1_Solution.html 160 Bytes
  • 5. TensorFlow - An introduction/7.5 TensorFlow_Minimal_Example_Exercise_3_Solution.html 160 Bytes
  • 5. TensorFlow - An introduction/7.7 TensorFlow_Minimal_Example_Exercise_4_Solution.html 160 Bytes
  • 12. The MNIST example/3.1 TensorFlow_MNIST_with_comments_Part_1.html 159 Bytes
  • 12. The MNIST example/4.1 TensorFlow_MNIST_with_comments_Part_2.html 159 Bytes
  • 12. The MNIST example/5.1 TensorFlow_MNIST_with_comments_Part_3.html 159 Bytes
  • 12. The MNIST example/6.1 TensorFlow_MNIST_with_comments_Part_4.html 159 Bytes
  • 12. The MNIST example/7.1 TensorFlow_MNIST_with_comments_Part_5.html 159 Bytes
  • 12. The MNIST example/8.1 TensorFlow_MNIST_with_comments_Part_6.html 159 Bytes
  • 12. The MNIST example/11.5 MNIST_around_98_percent_accuracy_solution.html 157 Bytes
  • 5. TensorFlow - An introduction/6.1 TensorFlow - Minimal example complete.html 156 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.d. Solution.html 154 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.4 Minimal_example_Exercise_3.b. Solution.html 154 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.7 Minimal_example_Exercise_3.c. Solution.html 154 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.9 Minimal_example_Exercise_3.a. Solution.html 154 Bytes
  • 5. TensorFlow - An introduction/3.1 TensorFlow Minimal example - Part 1.html 154 Bytes
  • 5. TensorFlow - An introduction/4.1 TensorFlow Minimal example - Part 2.html 154 Bytes
  • 5. TensorFlow - An introduction/5.1 TensorFlow Minimal example - Part 3.html 154 Bytes
  • 5. TensorFlow - An introduction/7.4 TensorFlow_Minimal_Example_All_Exercises.html 154 Bytes
  • 12. The MNIST example/9.1 TensorFlow_MNIST_with_comments.html 152 Bytes
  • 12. The MNIST example/11.1 MNIST_Depth_Solution.html 150 Bytes
  • 12. The MNIST example/11.7 MNIST_Width_Solution.html 150 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.1 Minimal_example_Exercise_2_Solution.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/5.3 Minimal_example_Exercise_4_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/5.8 Minimal_example_Exercise_5_Solution.html 149 Bytes
  • 14. Appendix Linear Algebra Fundamentals/5.1 Tensors Notebook.html 148 Bytes
  • 4. Minimal example - your first machine learning algorithm/4.1 Minimal example - part 4.html 145 Bytes
  • 12. The MNIST example/10.1 MNIST_Exercises_All.html 144 Bytes
  • 4. Minimal example - your first machine learning algorithm/5.5 Minimal_example_All_Exercises.html 143 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
  • 13. Business case/11.1 Homework exercise.html 134 Bytes
  • 13. Business case/12.1 Homework exercise.html 134 Bytes
  • 13. Business case/4.1 Preprocessing.html 134 Bytes
  • 13. Business case/5.1 Preprocessing exercise.html 134 Bytes
  • 13. Business case/6.1 Class.html 134 Bytes
  • 13. Business case/7.1 Outlining the model.html 134 Bytes
  • 13. Business case/8.1 Optimizing the algorithm.html 134 Bytes
  • 13. Business case/9.1 Interpreting the result.html 134 Bytes
  • [FCS Forum].url 133 Bytes
  • [FreeCourseSite.com].url 127 Bytes
  • [CourseClub.ME].url 122 Bytes

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