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Language:
English
Total Size:
1.1 GB
Info Hash:
918E88942344F445354A13B4C612132DF6BEF85E
Added By:
Added:
Oct. 24, 2023, 5:18 p.m.
Stats:
|
(Last updated: May 20, 2025, 7:09 a.m.)
| File | Size |
|---|---|
| 1. Introduction and Outline.mp4 | 42.7 MB |
| 1. Introduction and Outline.srt | 10.8 KB |
| 2. How to Succeed in this Course.mp4 | 43.8 MB |
| 2. How to Succeed in this Course.srt | 8.3 KB |
| 3. Statistics vs. Machine Learning.mp4 | 55.5 MB |
| 3. Statistics vs. Machine Learning.srt | 14.7 KB |
| [Tutorialsplanet.NET].url | 128 bytes |
| 1. What is machine learning How does linear regression play a role.mp4 | 8.4 MB |
| 1. What is machine learning How does linear regression play a role.srt | 5.8 KB |
| 10. Demonstrating Moore's Law in Code.mp4 | 17.5 MB |
| 10. Demonstrating Moore's Law in Code.srt | 6.9 KB |
| 11. Moore's Law Derivation.mp4 | 20.2 MB |
| 11. Moore's Law Derivation.srt | 7.6 KB |
| 12. R-squared Quiz 1.mp4 | 2.8 MB |
| 12. R-squared Quiz 1.srt | 2.2 KB |
| 13. Suggestion Box.mp4 | 16.1 MB |
| 13. Suggestion Box.srt | 4.7 KB |
| 2. What can linear regression be used for.html | 150 bytes |
| 3. Define the model in 1-D, derive the solution (Updated Version).mp4 | 19.4 MB |
| 3. Define the model in 1-D, derive the solution (Updated Version).srt | 16.5 KB |
| 4. Define the model in 1-D, derive the solution.mp4 | 24.7 MB |
| 4. Define the model in 1-D, derive the solution.srt | 11.1 KB |
| 5. Coding the 1-D solution in Python.mp4 | 14.4 MB |
| 5. Coding the 1-D solution in Python.srt | 5.6 KB |
| 6. Exercise Theory vs. Code.mp4 | 1.0 MB |
| 6. Exercise Theory vs. Code.srt | 1.6 KB |
| 7. Determine how good the model is - r-squared.mp4 | 11.3 MB |
| 7. Determine how good the model is - r-squared.srt | 4.7 KB |
| 8. R-squared in code.mp4 | 4.5 MB |
| 8. R-squared in code.srt | 1.7 KB |
| 9. Introduction to Moore's Law Problem.mp4 | 4.4 MB |
| 9. Introduction to Moore's Law Problem.srt | 3.7 KB |
| 1. Define the multi-dimensional problem and derive the solution (Updated Version).mp4 | 14.4 MB |
| 1. Define the multi-dimensional problem and derive the solution (Updated Version).srt | 11.7 KB |
| 2. Define the multi-dimensional problem and derive the solution.mp4 | 36.1 MB |
| 2. Define the multi-dimensional problem and derive the solution.srt | 12.9 KB |
| 3. How to solve multiple linear regression using only matrices.mp4 | 3.1 MB |
| 3. How to solve multiple linear regression using only matrices.srt | 2.0 KB |
| 4. Coding the multi-dimensional solution in Python.mp4 | 14.9 MB |
| 4. Coding the multi-dimensional solution in Python.srt | 5.2 KB |
| 5. Polynomial regression - extending linear regression (with Python code).mp4 | 16.4 MB |
| 5. Polynomial regression - extending linear regression (with Python code).srt | 4.9 KB |
| 6. Predicting Systolic Blood Pressure from Age and Weight.mp4 | 12.3 MB |
| 6. Predicting Systolic Blood Pressure from Age and Weight.srt | 5.5 KB |
| 7. R-squared Quiz 2.mp4 | 3.5 MB |
| 7. R-squared Quiz 2.srt | 2.7 KB |
| 1. What do all these letters mean.mp4 | 9.6 MB |
| 1. What do all these letters mean.srt | 8.0 KB |
| 10. The Dummy Variable Trap.mp4 | 6.1 MB |
| 10. The Dummy Variable Trap.srt | 5.5 KB |
| 11. Gradient Descent Tutorial.mp4 | 22.8 MB |
| 11. Gradient Descent Tutorial.srt | 5.5 KB |
| 12. Gradient Descent for Linear Regression.mp4 | 3.5 MB |
| 12. Gradient Descent for Linear Regression.srt | 3.1 KB |
| 13. Bypass the Dummy Variable Trap with Gradient Descent.mp4 | 8.5 MB |
| 13. Bypass the Dummy Variable Trap with Gradient Descent.srt | 3.6 KB |
| 14. L1 Regularization - Theory.mp4 | 4.7 MB |
| 14. L1 Regularization - Theory.srt | 4.1 KB |
| 15. L1 Regularization - Code.mp4 | 8.3 MB |
| 15. L1 Regularization - Code.srt | 3.5 KB |
| 16. L1 vs L2 Regularization.mp4 | 4.8 MB |
| 16. L1 vs L2 Regularization.srt | 4.3 KB |
| 17. Why Divide by Square Root of D.mp4 | 23.5 MB |
| 17. Why Divide by Square Root of D.srt | 8.7 KB |
| 2. Interpreting the Weights.mp4 | 14.2 MB |
| 2. Interpreting the Weights.srt | 4.3 KB |
| 3. Generalization error, train and test sets.mp4 | 4.4 MB |
| 3. Generalization error, train and test sets.srt | 2.8 KB |
| 4. Generalization and Overfitting Demonstration in Code.mp4 | 17.2 MB |
| 4. Generalization and Overfitting Demonstration in Code.srt | 9.2 KB |
| 5. Categorical inputs.mp4 | 8.2 MB |
| 5. Categorical inputs.srt | 4.8 KB |
| 6. One-Hot Encoding Quiz.mp4 | 3.8 MB |
| 6. One-Hot Encoding Quiz.srt | 2.5 KB |
| 7. Probabilistic Interpretation of Squared Error.mp4 | 8.1 MB |
| 7. Probabilistic Interpretation of Squared Error.srt | 6.4 KB |
| 8. L2 Regularization - Theory.mp4 | 6.7 MB |
| 8. L2 Regularization - Theory.srt | 5.5 KB |
| 9. L2 Regularization - Code.mp4 | 8.1 MB |
| 9. L2 Regularization - Code.srt | 3.4 KB |
| 1. Brief overview of advanced linear regression and machine learning topics.mp4 | 8.1 MB |
| 1. Brief overview of advanced linear regression and machine learning topics.srt | 5.7 KB |
| 2. Exercises, practice, and how to get good at this.mp4 | 7.2 MB |
| 2. Exercises, practice, and how to get good at this.srt | 5.3 KB |
| 1. Anaconda Environment Setup.mp4 | 186.3 MB |
| 1. Anaconda Environment Setup.srt | 20.1 KB |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 | 43.9 MB |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt | 14.5 KB |
| 1. How to Code by Yourself (part 1).mp4 | 24.5 MB |
| 1. How to Code by Yourself (part 1).srt | 22.8 KB |
| 2. How to Code by Yourself (part 2).mp4 | 14.8 MB |
| 2. How to Code by Yourself (part 2).srt | 13.3 KB |
| 3. Proof that using Jupyter Notebook is the same as not using it.mp4 | 78.3 MB |
| 3. Proof that using Jupyter Notebook is the same as not using it.srt | 14.1 KB |
| 4. Python 2 vs Python 3.mp4 | 7.8 MB |
| 4. Python 2 vs Python 3.srt | 6.1 KB |
| [Tutorialsplanet.NET].url | 128 bytes |
| 1. How to Succeed in this Course (Long Version).mp4 | 18.3 MB |
| 1. How to Succeed in this Course (Long Version).srt | 14.5 KB |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 | 39.0 MB |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt | 31.8 KB |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 | 29.3 MB |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt | 16.0 KB |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 | 37.6 MB |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt | 23.0 KB |
| 1. What is the Appendix.mp4 | 5.5 MB |
| 1. What is the Appendix.srt | 3.7 KB |
| 2. BONUS.mp4 | 37.8 MB |
| 2. BONUS.srt | 7.9 KB |
| [Tutorialsplanet.NET].url | 128 bytes |
Name
DL
Uploader
Size
S/L
Added
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702.0 MB
[2
/
1]
2025-03-07
| Uploaded by freecoursewb | Size 702.0 MB | Health [ 2 /1 ] | Added 2025-03-07 |
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SOURCE: Udemy Deep Learning Prerequisites Linear Regression in Python
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