[FTUForum.com] [UDEMY] Deep Learning Prerequisites Linear Regression in Python [FTU]

Creation Time	Sept. 8, 2023, 4:50 a.m.
Last Access Time	Sept. 21, 2024, 5:17 a.m.
File Size	999.5 MB
Keywords	FTU Linear Prerequisites FTUForum Deep UDEMY Python Learning in com Regression
Total Requests	41
Total Files	107
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

6. Appendix/3. Windows-Focused Environment Setup 2018.mp4 186.3 MB
6. Appendix/11. What order should I take your courses in (part 2).mp4 81.7 MB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39.0 MB
6. Appendix/11. What order should I take your courses in (part 2).vtt 37.6 MB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 36.1 MB
1. Welcome/1. Welcome.mp4 32.0 MB
6. Appendix/10. What order should I take your courses in (part 1).mp4 29.3 MB
1. Welcome/2. Introduction and Outline.mp4 28.4 MB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 24.7 MB
6. Appendix/5. How to Code by Yourself (part 1).mp4 24.5 MB
4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 22.8 MB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 19.3 MB
6. Appendix/7. How to Succeed in this Course (Long Version).mp4 18.3 MB
2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.mp4 17.5 MB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp4 17.3 MB
6. Appendix/12. Python 2 vs Python 3.mp4 16.9 MB
3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).mp4 16.4 MB
3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp4 14.9 MB
6. Appendix/6. How to Code by Yourself (part 2).mp4 14.8 MB
2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.mp4 14.4 MB
3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).mp4 14.4 MB
3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.mp4 12.3 MB
2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4 11.3 MB
4. Practical machine learning issues/1. What do all these letters mean.mp4 9.6 MB
4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.mp4 8.5 MB
1. Welcome/3. What is machine learning How does linear regression play a role.mp4 8.4 MB
4. Practical machine learning issues/15. L1 Regularization - Code.mp4 8.3 MB
4. Practical machine learning issues/5. Categorical inputs.mp4 8.2 MB
4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.mp4 8.1 MB
5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.mp4 8.1 MB
4. Practical machine learning issues/9. L2 Regularization - Code.mp4 8.1 MB
5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.mp4 7.2 MB
4. Practical machine learning issues/8. L2 Regularization - Theory.mp4 6.7 MB
4. Practical machine learning issues/10. The Dummy Variable Trap.mp4 6.1 MB
4. Practical machine learning issues/2. Interpreting the Weights.mp4 6.1 MB
6. Appendix/1. What is the Appendix.mp4 5.5 MB
4. Practical machine learning issues/16. L1 vs L2 Regularization.mp4 4.8 MB
4. Practical machine learning issues/14. L1 Regularization - Theory.mp4 4.7 MB
2. 1-D Linear Regression Theory and Code/6. R-squared in code.mp4 4.5 MB
1. Welcome/4. Introduction to Moore's Law Problem.mp4 4.4 MB
4. Practical machine learning issues/3. Generalization error, train and test sets.mp4 4.4 MB
6. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 4.0 MB
4. Practical machine learning issues/6. One-Hot Encoding Quiz.mp4 3.8 MB
4. Practical machine learning issues/12. Gradient Descent for Linear Regression.mp4 3.5 MB
3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.mp4 3.5 MB
1. Welcome/6. How to Succeed in this Course.mp4 3.3 MB
3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.mp4 3.1 MB
2. 1-D Linear Regression Theory and Code/8. R-squared Quiz 1.mp4 2.8 MB
2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.mp4 1.0 MB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 27.8 KB
6. Appendix/5. How to Code by Yourself (part 1).vtt 19.8 KB
6. Appendix/3. Windows-Focused Environment Setup 2018.vtt 17.4 KB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).vtt 14.4 KB
6. Appendix/10. What order should I take your courses in (part 1).vtt 14.1 KB
6. Appendix/7. How to Succeed in this Course (Long Version).vtt 12.8 KB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.4 KB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt 12.2 KB
6. Appendix/6. How to Code by Yourself (part 2).vtt 11.6 KB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.vtt 11.4 KB
3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).vtt 10.3 KB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.vtt 9.6 KB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.vtt 8.2 KB
4. Practical machine learning issues/1. What do all these letters mean.vtt 7.0 KB
2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.vtt 6.2 KB
4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.vtt 5.7 KB
6. Appendix/12. Python 2 vs Python 3.vtt 5.4 KB
1. Welcome/2. Introduction and Outline.vtt 5.3 KB
1. Welcome/3. What is machine learning How does linear regression play a role.vtt 5.3 KB
5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.vtt 5.1 KB
3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.vtt 4.9 KB
2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.vtt 4.9 KB
4. Practical machine learning issues/10. The Dummy Variable Trap.vtt 4.9 KB
4. Practical machine learning issues/8. L2 Regularization - Theory.vtt 4.8 KB
5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.vtt 4.8 KB
4. Practical machine learning issues/11. Gradient Descent Tutorial.vtt 4.8 KB
3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.vtt 4.5 KB
4. Practical machine learning issues/5. Categorical inputs.vtt 4.3 KB
3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).vtt 4.3 KB
2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.vtt 4.1 KB
1. Welcome/1. Welcome.vtt 4.0 KB
4. Practical machine learning issues/16. L1 vs L2 Regularization.vtt 3.7 KB
4. Practical machine learning issues/2. Interpreting the Weights.vtt 3.7 KB
4. Practical machine learning issues/14. L1 Regularization - Theory.vtt 3.6 KB
1. Welcome/6. How to Succeed in this Course.vtt 3.5 KB
1. Welcome/4. Introduction to Moore's Law Problem.vtt 3.4 KB
6. Appendix/1. What is the Appendix.vtt 3.3 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 3.0 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 2.2 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 1.8 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

[FTUForum.com] [UDEMY] Deep Learning Prerequisites Linear Regression in Python [FTU]

File List

Download Torrent

Related Resources

Copyright Infringement