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[UdemyCourseDownloader] Deep Learning with TensorFlow 2.0 [2019]
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2021-7-1 19:42
2024-12-22 11:38
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1.96 GB
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UdemyCourseDownloader
Deep
Learning
with
TensorFlow
2
0
2019
文件列表
14. Appendix Linear Algebra Fundamentals/11. Why is Linear Algebra Useful.mp4
144.34MB
01. Welcome! Course introduction/1. Meet your instructors and why you should study machine learning.mp4
105.79MB
01. Welcome! Course introduction/2. What does the course cover.mp4
16.36MB
02. Introduction to neural networks/1. Introduction to neural networks.mp4
13.56MB
02. Introduction to neural networks/3. Training the model.mp4
8.82MB
02. Introduction to neural networks/5. Types of machine learning.mp4
12.2MB
02. Introduction to neural networks/7. The linear model.mp4
9.13MB
02. Introduction to neural networks/10. The linear model. Multiple inputs.mp4
7.5MB
02. Introduction to neural networks/12. The linear model. Multiple inputs and multiple outputs.mp4
38.29MB
02. Introduction to neural networks/14. Graphical representation.mp4
6.35MB
02. Introduction to neural networks/16. The objective function.mp4
5.72MB
02. Introduction to neural networks/18. L2-norm loss.mp4
7.26MB
02. Introduction to neural networks/20. Cross-entropy loss.mp4
11.36MB
02. Introduction to neural networks/22. One parameter gradient descent.mp4
17.77MB
02. Introduction to neural networks/24. N-parameter gradient descent.mp4
39.45MB
03. Setting up the working environment/1. Setting up the environment - An introduction - Do not skip, please!.mp4
7.18MB
03. Setting up the working environment/2. Why Python and why Jupyter.mp4
41.02MB
03. Setting up the working environment/4. Installing Anaconda.mp4
34.91MB
03. Setting up the working environment/5. The Jupyter dashboard - part 1.mp4
9.5MB
03. Setting up the working environment/6. The Jupyter dashboard - part 2.mp4
21.08MB
03. Setting up the working environment/9. Installing TensorFlow 2.mp4
42.94MB
04. Minimal example - your first machine learning algorithm/1. Minimal example - part 1.mp4
6.53MB
04. Minimal example - your first machine learning algorithm/2. Minimal example - part 2.mp4
10.71MB
04. Minimal example - your first machine learning algorithm/3. Minimal example - part 3.mp4
9.77MB
04. Minimal example - your first machine learning algorithm/4. Minimal example - part 4.mp4
20.81MB
05. TensorFlow - An introduction/1. TensorFlow outline.mp4
38.32MB
05. TensorFlow - An introduction/2. TensorFlow 2 intro.mp4
25.07MB
05. TensorFlow - An introduction/3. A Note on Coding in TensorFlow.mp4
7.13MB
05. TensorFlow - An introduction/4. Types of file formats in TensorFlow and data handling.mp4
18.5MB
05. TensorFlow - An introduction/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4
38.22MB
05. TensorFlow - An introduction/6. Interpreting the result and extracting the weights and bias.mp4
32.82MB
05. TensorFlow - An introduction/7. Cutomizing your model.mp4
24.66MB
06. Going deeper Introduction to deep neural networks/1. Layers.mp4
4.74MB
06. Going deeper Introduction to deep neural networks/2. What is a deep net.mp4
6.73MB
06. Going deeper Introduction to deep neural networks/3. Understanding deep nets in depth.mp4
13.41MB
06. Going deeper Introduction to deep neural networks/4. Why do we need non-linearities.mp4
8.96MB
06. Going deeper Introduction to deep neural networks/5. Activation functions.mp4
8.74MB
06. Going deeper Introduction to deep neural networks/6. Softmax activation.mp4
7.38MB
06. Going deeper Introduction to deep neural networks/7. Backpropagation.mp4
11.06MB
06. Going deeper Introduction to deep neural networks/8. Backpropagation - visual representation.mp4
6.85MB
08. Overfitting/1. Underfitting and overfitting.mp4
11.06MB
08. Overfitting/2. Underfitting and overfitting - classification.mp4
6.77MB
08. Overfitting/3. Training and validation.mp4
9.23MB
08. Overfitting/4. Training, validation, and test.mp4
7.45MB
08. Overfitting/5. N-fold cross validation.mp4
6.99MB
08. Overfitting/6. Early stopping.mp4
9.44MB
09. Initialization/1. Initialization - Introduction.mp4
8.03MB
09. Initialization/2. Types of simple initializations.mp4
5.61MB
09. Initialization/3. Xavier initialization.mp4
5.83MB
10. Gradient descent and learning rates/1. Stochastic gradient descent.mp4
9.39MB
10. Gradient descent and learning rates/2. Gradient descent pitfalls.mp4
4.31MB
10. Gradient descent and learning rates/3. Momentum.mp4
6.11MB
10. Gradient descent and learning rates/4. Learning rate schedules.mp4
10.31MB
10. Gradient descent and learning rates/5. Learning rate schedules. A picture.mp4
3.14MB
10. Gradient descent and learning rates/6. Adaptive learning rate schedules.mp4
8.86MB
10. Gradient descent and learning rates/7. Adaptive moment estimation.mp4
7.78MB
11. Preprocessing/1. Preprocessing introduction.mp4
8.42MB
11. Preprocessing/2. Basic preprocessing.mp4
3.65MB
11. Preprocessing/3. Standardization.mp4
8.33MB
11. Preprocessing/4. Dealing with categorical data.mp4
6.08MB
11. Preprocessing/5. One-hot and binary encoding.mp4
6.24MB
12. The MNIST example/1. The dataset.mp4
15.67MB
12. The MNIST example/2. How to tackle the MNIST.mp4
20.4MB
12. The MNIST example/3. Importing the relevant packages and load the data.mp4
17.77MB
12. The MNIST example/4. Preprocess the data - create a validation dataset and scale the data.mp4
31.94MB
12. The MNIST example/6. Preprocess the data - shuffle and batch the data.mp4
45.93MB
12. The MNIST example/8. Outline the model.mp4
31.17MB
12. The MNIST example/9. Select the loss and the optimizer.mp4
15.26MB
12. The MNIST example/10. Learning.mp4
44.47MB
12. The MNIST example/13. Testing the model.mp4
32.49MB
13. Business case/1. Exploring the dataset and identifying predictors.mp4
78.16MB
13. Business case/2. Outlining the business case solution.mp4
7.95MB
13. Business case/3. Balancing the dataset.mp4
35.19MB
13. Business case/4. Preprocessing the data.mp4
92MB
13. Business case/6. Load the preprocessed data.mp4
19.38MB
13. Business case/8. Learning and interpreting the result.mp4
34.6MB
13. Business case/9. Setting an early stopping mechanism.mp4
53.36MB
13. Business case/11. Testing the model.mp4
12.07MB
14. Appendix Linear Algebra Fundamentals/1. What is a Matrix.mp4
33.59MB
14. Appendix Linear Algebra Fundamentals/2. Scalars and Vectors.mp4
33.84MB
14. Appendix Linear Algebra Fundamentals/3. Linear Algebra and Geometry.mp4
49.79MB
14. Appendix Linear Algebra Fundamentals/4. Scalars, Vectors and Matrices in Python.mp4
26.67MB
14. Appendix Linear Algebra Fundamentals/5. Tensors.mp4
22.51MB
14. Appendix Linear Algebra Fundamentals/6. Addition and Subtraction of Matrices.mp4
32.61MB
14. Appendix Linear Algebra Fundamentals/7. Errors when Adding Matrices.mp4
11.17MB
14. Appendix Linear Algebra Fundamentals/8. Transpose of a Matrix.mp4
38.09MB
14. Appendix Linear Algebra Fundamentals/9. Dot Product of Vectors.mp4
23.99MB
14. Appendix Linear Algebra Fundamentals/10. Dot Product of Matrices.mp4
49.38MB
15. Conclusion/1. See how much you have learned.mp4
13.96MB
15. Conclusion/2. What’s further out there in the machine and deep learning world.mp4
6.26MB
15. Conclusion/3. An overview of CNNs.mp4
10.93MB
15. Conclusion/5. An overview of RNNs.mp4
4.86MB
15. Conclusion/6. An overview of non-NN approaches.mp4
7.85MB
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