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种子名称： [DesireCourse.Net] Udemy - Master Deep Learning with TensorFlow in Python
文件类型： 视频
文件数目： 94个文件
文件大小： 1.41 GB
收录时间： 2022-11-13 05:48
已经下载： 3次
资源热度： 115
最近下载： 2024-6-17 16:10

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[DesireCourse.Net] Udemy - Master Deep Learning with TensorFlow in Python.torrent

1. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.mp4105.79MB

1. Welcome! Course introduction/2. What does the course cover.mp416.36MB

10. Gradient descent and learning rates/1. Stochastic gradient descent.mp49.38MB

10. Gradient descent and learning rates/2. Gradient descent pitfalls.mp44.31MB

10. Gradient descent and learning rates/3. Momentum.mp46.11MB

10. Gradient descent and learning rates/4. Learning rate schedules.mp410.3MB

10. Gradient descent and learning rates/5. Learning rate schedules. A picture.mp43.15MB

10. Gradient descent and learning rates/6. Adaptive learning rate schedules.mp48.86MB

10. Gradient descent and learning rates/7. Adaptive moment estimation.mp47.78MB

11. Preprocessing/1. Preprocessing introduction.mp48.42MB

11. Preprocessing/2. Basic preprocessing.mp43.65MB

11. Preprocessing/3. Standardization.mp48.33MB

11. Preprocessing/4. Dealing with categorical data.mp46.08MB

11. Preprocessing/5. One-hot and binary encoding.mp46.24MB

12. The MNIST example/1. The dataset.mp47.37MB

12. The MNIST example/2. How to tackle the MNIST.mp47.3MB

12. The MNIST example/3. Importing the relevant packages.mp45.46MB

12. The MNIST example/4. Outlining the model.mp418.37MB

12. The MNIST example/5. Declaring the loss and the optimization algorithm.mp47.14MB

12. The MNIST example/6. Accuracy of prediction.mp412.38MB

12. The MNIST example/7. Batching and early stopping.mp44.58MB

12. The MNIST example/8. Learning.mp415.9MB

12. The MNIST example/9. Discuss the results and test.mp421.97MB

13. Business case/1. Exploring the dataset and identifying predictors.mp423.26MB

13. Business case/10. Testing the model.mp44.29MB

13. Business case/11. A comment on the homework.mp413.01MB

13. Business case/2. Outlining the business case solution.mp43.84MB

13. Business case/3. Balancing the dataset.mp413.81MB

13. Business case/4. Preprocessing the data.mp434.33MB

13. Business case/6. Create a class for batching.mp427.65MB

13. Business case/7. Outlining the model.mp419.46MB

13. Business case/8. Optimizing the algorithm.mp412.22MB

13. Business case/9. Interpreting the result.mp45.35MB

14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp433.59MB

14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.mp449.38MB

14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4144.33MB

14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.mp433.84MB

14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.mp449.8MB

14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.mp426.67MB

14. Appendix Linear Algebra Fundamentals/5. Tensors.mp422.52MB

14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp432.61MB

14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.mp411.17MB

14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.mp438.08MB

14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp423.99MB

15. Conclusion/1. See how much you have learned.mp413.96MB

15. Conclusion/2. What’s further out there in the machine and deep learning world.mp46.27MB

15. Conclusion/3. An overview of CNNs.mp410.93MB

15. Conclusion/5. An overview of RNNs.mp44.86MB

15. Conclusion/6. An overview of non-NN approaches.mp47.84MB

2. Introduction to neural networks/1. Introduction to neural networks.mp413.56MB

2. Introduction to neural networks/10. The linear model. Multiple inputs.mp47.5MB

2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp438.29MB

2. Introduction to neural networks/14. Graphical representation.mp46.35MB

2. Introduction to neural networks/16. The objective function.mp45.72MB

2. Introduction to neural networks/18. L2-norm loss.mp47.27MB

2. Introduction to neural networks/20. Cross-entropy loss.mp411.36MB

2. Introduction to neural networks/22. One parameter gradient descent.mp417.76MB

2. Introduction to neural networks/24. N-parameter gradient descent.mp439.46MB

2. Introduction to neural networks/3. Training the model.mp48.81MB

2. Introduction to neural networks/5. Types of machine learning.mp412.21MB

2. Introduction to neural networks/7. The linear model.mp49.13MB

3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp42.62MB

3. Setting up the working environment/2. Why Python and why Jupyter.mp413.63MB

3. Setting up the working environment/4. Installing Anaconda.mp49.39MB

3. Setting up the working environment/5. The Jupyter dashboard - part 1.mp45.59MB

3. Setting up the working environment/6. The Jupyter dashboard - part 2.mp410.92MB

3. Setting up the working environment/9. Installing the TensorFlow package.mp44.86MB

4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp46.54MB

4. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.mp410.71MB

4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp49.76MB

4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp420.8MB

5. TensorFlow - An introduction/1. TensorFlow outline.mp414.47MB

5. TensorFlow - An introduction/2. TensorFlow intro.mp47.54MB

5. TensorFlow - An introduction/3. Types of file formats in TensorFlow.mp45.83MB

5. TensorFlow - An introduction/4. Inputs, outputs, targets, weights, biases - model layout.mp412.95MB

5. TensorFlow - An introduction/5. Loss function and gradient descent - introducing optimizers.mp49.7MB

5. TensorFlow - An introduction/6. Model output.mp414.33MB

6. Going deeper Introduction to deep neural networks/1. Layers.mp44.74MB

6. Going deeper Introduction to deep neural networks/2. What is a deep net.mp46.72MB

6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp413.41MB

6. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.mp48.96MB

6. Going deeper Introduction to deep neural networks/5. Activation functions.mp48.74MB

6. Going deeper Introduction to deep neural networks/6. Softmax activation.mp47.37MB

6. Going deeper Introduction to deep neural networks/7. Backpropagation.mp411.06MB

6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.mp46.85MB

8. Overfitting/1. Underfitting and overfitting.mp411.06MB

8. Overfitting/2. Underfitting and overfitting - classification.mp46.76MB

8. Overfitting/3. Training and validation.mp49.24MB

8. Overfitting/4. Training, validation, and test.mp47.44MB

8. Overfitting/5. N-fold cross validation.mp46.99MB

8. Overfitting/6. Early stopping.mp49.43MB

9. Initialization/1. Initialization - Introduction.mp48.04MB

9. Initialization/2. Types of simple initializations.mp45.62MB

9. Initialization/3. Xavier initialization.mp45.82MB