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[FTUForum.com] [UDEMY] Deep Learning Prerequisites Linear Regression in Python [FTU]
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文档列表
6. Appendix/3. Windows-Focused Environment Setup 2018.mp4
195.3 MB
6. Appendix/11. What order should I take your courses in (part 2).mp4
85.6 MB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4
82.1 MB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4
46.1 MB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4
40.9 MB
6. Appendix/11. What order should I take your courses in (part 2).vtt
39.5 MB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4
37.8 MB
1. Welcome/1. Welcome.mp4
33.6 MB
6. Appendix/10. What order should I take your courses in (part 1).mp4
30.7 MB
1. Welcome/2. Introduction and Outline.mp4
29.8 MB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4
25.9 MB
6. Appendix/5. How to Code by Yourself (part 1).mp4
25.7 MB
4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4
23.9 MB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4
20.3 MB
6. Appendix/7. How to Succeed in this Course (Long Version).mp4
19.2 MB
2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.mp4
18.3 MB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp4
18.1 MB
6. Appendix/12. Python 2 vs Python 3.mp4
17.7 MB
3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).mp4
17.2 MB
3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp4
15.6 MB
6. Appendix/6. How to Code by Yourself (part 2).mp4
15.5 MB
2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.mp4
15.1 MB
3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).mp4
15.1 MB
3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.mp4
12.9 MB
2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4
11.9 MB
4. Practical machine learning issues/1. What do all these letters mean.mp4
10.1 MB
4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.mp4
8.9 MB
1. Welcome/3. What is machine learning How does linear regression play a role.mp4
8.8 MB
4. Practical machine learning issues/15. L1 Regularization - Code.mp4
8.7 MB
4. Practical machine learning issues/5. Categorical inputs.mp4
8.6 MB
4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.mp4
8.5 MB
5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.mp4
8.5 MB
4. Practical machine learning issues/9. L2 Regularization - Code.mp4
8.5 MB
5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.mp4
7.5 MB
4. Practical machine learning issues/8. L2 Regularization - Theory.mp4
7.0 MB
4. Practical machine learning issues/10. The Dummy Variable Trap.mp4
6.4 MB
4. Practical machine learning issues/2. Interpreting the Weights.mp4
6.3 MB
6. Appendix/1. What is the Appendix.mp4
5.7 MB
4. Practical machine learning issues/16. L1 vs L2 Regularization.mp4
5.0 MB
4. Practical machine learning issues/14. L1 Regularization - Theory.mp4
4.9 MB
2. 1-D Linear Regression Theory and Code/6. R-squared in code.mp4
4.7 MB
1. Welcome/4. Introduction to Moore's Law Problem.mp4
4.6 MB
4. Practical machine learning issues/3. Generalization error, train and test sets.mp4
4.6 MB
6. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4
4.2 MB
4. Practical machine learning issues/6. One-Hot Encoding Quiz.mp4
4.0 MB
4. Practical machine learning issues/12. Gradient Descent for Linear Regression.mp4
3.7 MB
3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.mp4
3.7 MB
1. Welcome/6. How to Succeed in this Course.mp4
3.5 MB
3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.mp4
3.3 MB
2. 1-D Linear Regression Theory and Code/8. R-squared Quiz 1.mp4
2.9 MB
2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.mp4
1.1 MB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt
28.4 kB
6. Appendix/5. How to Code by Yourself (part 1).vtt
20.3 kB
6. Appendix/3. Windows-Focused Environment Setup 2018.vtt
17.8 kB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).vtt
14.7 kB
6. Appendix/10. What order should I take your courses in (part 1).vtt
14.4 kB
6. Appendix/7. How to Succeed in this Course (Long Version).vtt
13.1 kB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt
12.7 kB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt
12.5 kB
6. Appendix/6. How to Code by Yourself (part 2).vtt
11.9 kB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.vtt
11.6 kB
3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).vtt
10.5 kB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.vtt
9.8 kB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.vtt
8.4 kB
4. Practical machine learning issues/1. What do all these letters mean.vtt
7.2 kB
2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.vtt
6.3 kB
4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.vtt
5.8 kB
6. Appendix/12. Python 2 vs Python 3.vtt
5.5 kB
1. Welcome/2. Introduction and Outline.vtt
5.4 kB
1. Welcome/3. What is machine learning How does linear regression play a role.vtt
5.4 kB
5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.vtt
5.2 kB
3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.vtt
5.0 kB
2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.vtt
5.0 kB
4. Practical machine learning issues/10. The Dummy Variable Trap.vtt
5.0 kB
4. Practical machine learning issues/8. L2 Regularization - Theory.vtt
5.0 kB
5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.vtt
4.9 kB
4. Practical machine learning issues/11. Gradient Descent Tutorial.vtt
4.9 kB
3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.vtt
4.6 kB
4. Practical machine learning issues/5. Categorical inputs.vtt
4.4 kB
3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).vtt
4.4 kB
2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.vtt
4.2 kB
1. Welcome/1. Welcome.vtt
4.1 kB
4. Practical machine learning issues/16. L1 vs L2 Regularization.vtt
3.8 kB
4. Practical machine learning issues/2. Interpreting the Weights.vtt
3.8 kB
4. Practical machine learning issues/14. L1 Regularization - Theory.vtt
3.7 kB
1. Welcome/6. How to Succeed in this Course.vtt
3.6 kB
1. Welcome/4. Introduction to Moore's Law Problem.vtt
3.5 kB
6. Appendix/1. What is the Appendix.vtt
3.4 kB
4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.vtt
3.1 kB
4. Practical machine learning issues/15. L1 Regularization - Code.vtt
3.1 kB
6. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.vtt
3.1 kB
4. Practical machine learning issues/9. L2 Regularization - Code.vtt
3.0 kB
4. Practical machine learning issues/12. Gradient Descent for Linear Regression.vtt
2.8 kB
4. Practical machine learning issues/3. Generalization error, train and test sets.vtt
2.6 kB
3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.vtt
2.4 kB
4. Practical machine learning issues/6. One-Hot Encoding Quiz.vtt
2.3 kB
2. 1-D Linear Regression Theory and Code/8. R-squared Quiz 1.vtt
2.0 kB
3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.vtt
1.9 kB
2. 1-D Linear Regression Theory and Code/6. R-squared in code.vtt
1.5 kB
2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.vtt
1.4 kB
0. Websites you may like/1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url
328 Bytes
0. Websites you may like/5. (Discuss.FTUForum.com) FTU Discussion Forum.url
294 Bytes
0. Websites you may like/2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url
286 Bytes
0. Websites you may like/4. (FTUApps.com) Download Cracked Developers Applications For Free.url
239 Bytes
0. Websites you may like/How you can help Team-FTU.txt
237 Bytes
0. Websites you may like/3. (NulledPremium.com) Download Cracked Website Themes, Plugins, Scripts And Stock Images.url
163 Bytes
1. Welcome/5. What can linear regression be used for.html
143 Bytes
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