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[FreeCourseSite.com] Udemy - Master Deep Learning with TensorFlow in Python
magnet:?xt=urn:btih:f56ad7b56cd1f8d8b57448dc482a09d53c8d768c&dn=[FreeCourseSite.com] Udemy - Master Deep Learning with TensorFlow in Python
磁力链接详情
Hash值:
f56ad7b56cd1f8d8b57448dc482a09d53c8d768c
点击数:
162
文件大小:
1.41 GB
文件数量:
94
创建日期:
2019-5-27 16:28
最后访问:
2025-1-15 03:51
访问标签:
FreeCourseSite
com
Udemy
-
Master
Deep
Learning
with
TensorFlow
in
Python
文件列表详情
1. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.mp4 105.79 MB
1. Welcome! Course introduction/2. What does the course cover.mp4 16.36 MB
10. Gradient descent and learning rates/1. Stochastic gradient descent.mp4 9.38 MB
10. Gradient descent and learning rates/2. Gradient descent pitfalls.mp4 4.31 MB
10. Gradient descent and learning rates/3. Momentum.mp4 6.11 MB
10. Gradient descent and learning rates/4. Learning rate schedules.mp4 10.3 MB
10. Gradient descent and learning rates/5. Learning rate schedules. A picture.mp4 3.15 MB
10. Gradient descent and learning rates/6. Adaptive learning rate schedules.mp4 8.86 MB
10. Gradient descent and learning rates/7. Adaptive moment estimation.mp4 7.78 MB
11. Preprocessing/1. Preprocessing introduction.mp4 8.42 MB
11. Preprocessing/2. Basic preprocessing.mp4 3.65 MB
11. Preprocessing/3. Standardization.mp4 8.33 MB
11. Preprocessing/4. Dealing with categorical data.mp4 6.08 MB
11. Preprocessing/5. One-hot and binary encoding.mp4 6.24 MB
12. The MNIST example/1. The dataset.mp4 7.37 MB
12. The MNIST example/2. How to tackle the MNIST.mp4 7.3 MB
12. The MNIST example/3. Importing the relevant packages.mp4 5.46 MB
12. The MNIST example/4. Outlining the model.mp4 18.37 MB
12. The MNIST example/5. Declaring the loss and the optimization algorithm.mp4 7.14 MB
12. The MNIST example/6. Accuracy of prediction.mp4 12.38 MB
12. The MNIST example/7. Batching and early stopping.mp4 4.58 MB
12. The MNIST example/8. Learning.mp4 15.9 MB
12. The MNIST example/9. Discuss the results and test.mp4 21.97 MB
13. Business case/1. Exploring the dataset and identifying predictors.mp4 23.26 MB
13. Business case/10. Testing the model.mp4 4.29 MB
13. Business case/11. A comment on the homework.mp4 13.01 MB
13. Business case/2. Outlining the business case solution.mp4 3.84 MB
13. Business case/3. Balancing the dataset.mp4 13.81 MB
13. Business case/4. Preprocessing the data.mp4 34.33 MB
13. Business case/6. Create a class for batching.mp4 27.65 MB
13. Business case/7. Outlining the model.mp4 19.46 MB
13. Business case/8. Optimizing the algorithm.mp4 12.22 MB
13. Business case/9. Interpreting the result.mp4 5.35 MB
14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp4 33.59 MB
14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.mp4 49.38 MB
14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4 144.33 MB
14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.mp4 33.84 MB
14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.mp4 49.8 MB
14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.mp4 26.67 MB
14. Appendix Linear Algebra Fundamentals/5. Tensors.mp4 22.52 MB
14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp4 32.61 MB
14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.mp4 11.17 MB
14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.mp4 38.08 MB
14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp4 23.99 MB
15. Conclusion/1. See how much you have learned.mp4 13.96 MB
15. Conclusion/2. What’s further out there in the machine and deep learning world.mp4 6.27 MB
15. Conclusion/3. An overview of CNNs.mp4 10.93 MB
15. Conclusion/5. An overview of RNNs.mp4 4.86 MB
15. Conclusion/6. An overview of non-NN approaches.mp4 7.84 MB
2. Introduction to neural networks/1. Introduction to neural networks.mp4 13.56 MB
2. Introduction to neural networks/10. The linear model. Multiple inputs.mp4 7.5 MB
2. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp4 38.29 MB
2. Introduction to neural networks/14. Graphical representation.mp4 6.35 MB
2. Introduction to neural networks/16. The objective function.mp4 5.72 MB
2. Introduction to neural networks/18. L2-norm loss.mp4 7.27 MB
2. Introduction to neural networks/20. Cross-entropy loss.mp4 11.36 MB
2. Introduction to neural networks/22. One parameter gradient descent.mp4 17.76 MB
2. Introduction to neural networks/24. N-parameter gradient descent.mp4 39.46 MB
2. Introduction to neural networks/3. Training the model.mp4 8.81 MB
2. Introduction to neural networks/5. Types of machine learning.mp4 12.21 MB
2. Introduction to neural networks/7. The linear model.mp4 9.13 MB
3. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp4 2.62 MB
3. Setting up the working environment/2. Why Python and why Jupyter.mp4 13.63 MB
3. Setting up the working environment/4. Installing Anaconda.mp4 9.39 MB
3. Setting up the working environment/5. The Jupyter dashboard - part 1.mp4 5.59 MB
3. Setting up the working environment/6. The Jupyter dashboard - part 2.mp4 10.92 MB
3. Setting up the working environment/9. Installing the TensorFlow package.mp4 4.86 MB
4. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp4 6.54 MB
4. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.mp4 10.71 MB
4. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp4 9.76 MB
4. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp4 20.8 MB
5. TensorFlow - An introduction/1. TensorFlow outline.mp4 14.47 MB
5. TensorFlow - An introduction/2. TensorFlow intro.mp4 7.54 MB
5. TensorFlow - An introduction/3. Types of file formats in TensorFlow.mp4 5.83 MB
5. TensorFlow - An introduction/4. Inputs, outputs, targets, weights, biases - model layout.mp4 12.95 MB
5. TensorFlow - An introduction/5. Loss function and gradient descent - introducing optimizers.mp4 9.7 MB
5. TensorFlow - An introduction/6. Model output.mp4 14.33 MB
6. Going deeper Introduction to deep neural networks/1. Layers.mp4 4.74 MB
6. Going deeper Introduction to deep neural networks/2. What is a deep net.mp4 6.72 MB
6. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp4 13.41 MB
6. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.mp4 8.96 MB
6. Going deeper Introduction to deep neural networks/5. Activation functions.mp4 8.74 MB
6. Going deeper Introduction to deep neural networks/6. Softmax activation.mp4 7.37 MB
6. Going deeper Introduction to deep neural networks/7. Backpropagation.mp4 11.06 MB
6. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.mp4 6.85 MB
8. Overfitting/1. Underfitting and overfitting.mp4 11.06 MB
8. Overfitting/2. Underfitting and overfitting - classification.mp4 6.76 MB
8. Overfitting/3. Training and validation.mp4 9.24 MB
8. Overfitting/4. Training, validation, and test.mp4 7.44 MB
8. Overfitting/5. N-fold cross validation.mp4 6.99 MB
8. Overfitting/6. Early stopping.mp4 9.43 MB
9. Initialization/1. Initialization - Introduction.mp4 8.04 MB
9. Initialization/2. Types of simple initializations.mp4 5.62 MB
9. Initialization/3. Xavier initialization.mp4 5.82 MB
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