[FreeCourseLab.com] Udemy - Deep Learning Prerequisites Linear Regression in Python

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[FreeCourseLab.com] Udemy - Deep Learning Prerequisites Linear Regression in Python

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种子哈希：9eddd4119b64e840b18f625f301104cb21cce31f
文件大小：942.01M
已经下载：751次
下载速度：极快
收录时间：2021-03-23
最近下载：2024-12-16

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文件列表

  • 6. Appendix/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
  • 6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
  • 1. Welcome/1. Welcome.mp4 52.1 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
  • 6. Appendix/11. What order should I take your courses in (part 2).mp4 39.4 MB
  • 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 37.8 MB
  • 6. Appendix/10. What order should I take your courses in (part 1).mp4 30.7 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
  • 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
  • 6. Appendix/12. Python 2 vs Python 3.mp4 8.2 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
  • 1. Welcome/2. Introduction and Outline.mp4 6.6 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
  • 1. Welcome/5. What can linear regression be used for.html 143 Bytes
  • [FreeCourseLab.com].url 126 Bytes

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