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种子名称： [UdemyCourseDownloader] Deep Learning with TensorFlow 2.0 [2019]
文件类型： 视频
文件数目： 93个文件
文件大小： 1.96 GB
收录时间： 2021-7-1 19:42
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资源热度： 198
最近下载： 2024-12-18 13:16

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[UdemyCourseDownloader] Deep Learning with TensorFlow 2.0 [2019].torrent

14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4144.34MB

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

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

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

02. Introduction to neural networks/3. Training the model.mp48.82MB

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

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

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

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

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

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

02. Introduction to neural networks/18. L2-norm loss.mp47.26MB

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

02. Introduction to neural networks/22. One parameter gradient descent.mp417.77MB

02. Introduction to neural networks/24. N-parameter gradient descent.mp439.45MB

03. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp47.18MB

03. Setting up the working environment/2. Why Python and why Jupyter.mp441.02MB

03. Setting up the working environment/4. Installing Anaconda.mp434.91MB

03. Setting up the working environment/5. The Jupyter dashboard - part 1.mp49.5MB

03. Setting up the working environment/6. The Jupyter dashboard - part 2.mp421.08MB

03. Setting up the working environment/9. Installing TensorFlow 2.mp442.94MB

04. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp46.53MB

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

04. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp49.77MB

04. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp420.81MB

05. TensorFlow - An introduction/1. TensorFlow outline.mp438.32MB

05. TensorFlow - An introduction/2. TensorFlow 2 intro.mp425.07MB

05. TensorFlow - An introduction/3. A Note on Coding in TensorFlow.mp47.13MB

05. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.mp418.5MB

05. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp438.22MB

05. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.mp432.82MB

05. TensorFlow - An introduction/7. Cutomizing your model.mp424.66MB

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

06. Going deeper Introduction to deep neural networks/2. What is a deep net.mp46.73MB

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

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

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

06. Going deeper Introduction to deep neural networks/6. Softmax activation.mp47.38MB

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

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

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

08. Overfitting/2. Underfitting and overfitting - classification.mp46.77MB

08. Overfitting/3. Training and validation.mp49.23MB

08. Overfitting/4. Training, validation, and test.mp47.45MB

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

08. Overfitting/6. Early stopping.mp49.44MB

09. Initialization/1. Initialization - Introduction.mp48.03MB

09. Initialization/2. Types of simple initializations.mp45.61MB

09. Initialization/3. Xavier initialization.mp45.83MB

10. Gradient descent and learning rates/1. Stochastic gradient descent.mp49.39MB

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.mp410.31MB

10. Gradient descent and learning rates/5. Learning rate schedules. A picture.mp43.14MB

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.mp415.67MB

12. The MNIST example/2. How to tackle the MNIST.mp420.4MB

12. The MNIST example/3. Importing the relevant packages and load the data.mp417.77MB

12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.mp431.94MB

12. The MNIST example/6. Preprocess the data - shuffle and batch the data.mp445.93MB

12. The MNIST example/8. Outline the model.mp431.17MB

12. The MNIST example/9. Select the loss and the optimizer.mp415.26MB

12. The MNIST example/10. Learning.mp444.47MB

12. The MNIST example/13. Testing the model.mp432.49MB

13. Business case/1. Exploring the dataset and identifying predictors.mp478.16MB

13. Business case/2. Outlining the business case solution.mp47.95MB

13. Business case/3. Balancing the dataset.mp435.19MB

13. Business case/4. Preprocessing the data.mp492MB

13. Business case/6. Load the preprocessed data.mp419.38MB

13. Business case/8. Learning and interpreting the result.mp434.6MB

13. Business case/9. Setting an early stopping mechanism.mp453.36MB

13. Business case/11. Testing the model.mp412.07MB

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

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

14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.mp449.79MB

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

14. Appendix Linear Algebra Fundamentals/5. Tensors.mp422.51MB

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.mp438.09MB

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

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

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.mp46.26MB

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.mp47.85MB