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[UdemyCourseDownloader] Deep Learning with TensorFlow 2.0 [2019]
magnet:?xt=urn:btih:c21e69cf7d6e2cba5fbc345eda84075b7bdbe25a&dn=[UdemyCourseDownloader] Deep Learning with TensorFlow 2.0 [2019]
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c21e69cf7d6e2cba5fbc345eda84075b7bdbe25a
infohash:
93
文件数量
1.96 GB
文件大小
2021-7-1 19:42
创建日期
2024-7-6 00:42
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相关分词
UdemyCourseDownloader
Deep
Learning
with
TensorFlow
2
0
2019
14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4 144.34 MB
01. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.mp4 105.79 MB
01. Welcome! Course introduction/2. What does the course cover.mp4 16.36 MB
02. Introduction to neural networks/1. Introduction to neural networks.mp4 13.56 MB
02. Introduction to neural networks/3. Training the model.mp4 8.82 MB
02. Introduction to neural networks/5. Types of machine learning.mp4 12.2 MB
02. Introduction to neural networks/7. The linear model.mp4 9.13 MB
02. Introduction to neural networks/10. The linear model. Multiple inputs.mp4 7.5 MB
02. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp4 38.29 MB
02. Introduction to neural networks/14. Graphical representation.mp4 6.35 MB
02. Introduction to neural networks/16. The objective function.mp4 5.72 MB
02. Introduction to neural networks/18. L2-norm loss.mp4 7.26 MB
02. Introduction to neural networks/20. Cross-entropy loss.mp4 11.36 MB
02. Introduction to neural networks/22. One parameter gradient descent.mp4 17.77 MB
02. Introduction to neural networks/24. N-parameter gradient descent.mp4 39.45 MB
03. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp4 7.18 MB
03. Setting up the working environment/2. Why Python and why Jupyter.mp4 41.02 MB
03. Setting up the working environment/4. Installing Anaconda.mp4 34.91 MB
03. Setting up the working environment/5. The Jupyter dashboard - part 1.mp4 9.5 MB
03. Setting up the working environment/6. The Jupyter dashboard - part 2.mp4 21.08 MB
03. Setting up the working environment/9. Installing TensorFlow 2.mp4 42.94 MB
04. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp4 6.53 MB
04. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.mp4 10.71 MB
04. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp4 9.77 MB
04. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp4 20.81 MB
05. TensorFlow - An introduction/1. TensorFlow outline.mp4 38.32 MB
05. TensorFlow - An introduction/2. TensorFlow 2 intro.mp4 25.07 MB
05. TensorFlow - An introduction/3. A Note on Coding in TensorFlow.mp4 7.13 MB
05. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.mp4 18.5 MB
05. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 38.22 MB
05. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.mp4 32.82 MB
05. TensorFlow - An introduction/7. Cutomizing your model.mp4 24.66 MB
06. Going deeper Introduction to deep neural networks/1. Layers.mp4 4.74 MB
06. Going deeper Introduction to deep neural networks/2. What is a deep net.mp4 6.73 MB
06. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp4 13.41 MB
06. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.mp4 8.96 MB
06. Going deeper Introduction to deep neural networks/5. Activation functions.mp4 8.74 MB
06. Going deeper Introduction to deep neural networks/6. Softmax activation.mp4 7.38 MB
06. Going deeper Introduction to deep neural networks/7. Backpropagation.mp4 11.06 MB
06. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.mp4 6.85 MB
08. Overfitting/1. Underfitting and overfitting.mp4 11.06 MB
08. Overfitting/2. Underfitting and overfitting - classification.mp4 6.77 MB
08. Overfitting/3. Training and validation.mp4 9.23 MB
08. Overfitting/4. Training, validation, and test.mp4 7.45 MB
08. Overfitting/5. N-fold cross validation.mp4 6.99 MB
08. Overfitting/6. Early stopping.mp4 9.44 MB
09. Initialization/1. Initialization - Introduction.mp4 8.03 MB
09. Initialization/2. Types of simple initializations.mp4 5.61 MB
09. Initialization/3. Xavier initialization.mp4 5.83 MB
10. Gradient descent and learning rates/1. Stochastic gradient descent.mp4 9.39 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 10.31 MB
10. Gradient descent and learning rates/5. Learning rate schedules. A picture.mp4 3.14 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 15.67 MB
12. The MNIST example/2. How to tackle the MNIST.mp4 20.4 MB
12. The MNIST example/3. Importing the relevant packages and load the data.mp4 17.77 MB
12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.mp4 31.94 MB
12. The MNIST example/6. Preprocess the data - shuffle and batch the data.mp4 45.93 MB
12. The MNIST example/8. Outline the model.mp4 31.17 MB
12. The MNIST example/9. Select the loss and the optimizer.mp4 15.26 MB
12. The MNIST example/10. Learning.mp4 44.47 MB
12. The MNIST example/13. Testing the model.mp4 32.49 MB
13. Business case/1. Exploring the dataset and identifying predictors.mp4 78.16 MB
13. Business case/2. Outlining the business case solution.mp4 7.95 MB
13. Business case/3. Balancing the dataset.mp4 35.19 MB
13. Business case/4. Preprocessing the data.mp4 92 MB
13. Business case/6. Load the preprocessed data.mp4 19.38 MB
13. Business case/8. Learning and interpreting the result.mp4 34.6 MB
13. Business case/9. Setting an early stopping mechanism.mp4 53.36 MB
13. Business case/11. Testing the model.mp4 12.07 MB
14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp4 33.59 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 49.79 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 22.51 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 38.09 MB
14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp4 23.99 MB
14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.mp4 49.38 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 6.26 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 7.85 MB
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