[Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Linear Regression in Python

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[Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Linear Regression in Python

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收录时间：2022-01-15
最近下载：2025-07-22

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文件列表

  • 6. Setting Up Your Environment (FAQ by Student Request)/1. Anaconda Environment Setup.mp4 195.3 MB
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
  • 1. Welcome/3. Statistics vs. Machine Learning.mp4 58.2 MB
  • 6. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
  • 1. Welcome/2. How to Succeed in this Course.mp4 46.0 MB
  • 1. Welcome/1. Introduction and Outline.mp4 44.8 MB
  • 8/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
  • 9. Appendix FAQ Finale/2. BONUS.mp4 39.7 MB
  • 8/4. Machine Learning and AI Prerequisite Roadmap (pt 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
  • 8/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 30.7 MB
  • 2. 1-D Linear Regression Theory and Code/4. Define the model in 1-D, derive the solution.mp4 25.9 MB
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp4 25.7 MB
  • 4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 24.6 MB
  • 4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23.9 MB
  • 2. 1-D Linear Regression Theory and Code/11. Moore's Law Derivation.mp4 21.2 MB
  • 2. 1-D Linear Regression Theory and Code/3. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB
  • 8/1. How to Succeed in this Course (Long Version).mp4 19.2 MB
  • 2. 1-D Linear Regression Theory and Code/10. 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
  • 2. 1-D Linear Regression Theory and Code/13. Suggestion Box.mp4 16.9 MB
  • 3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp4 15.6 MB
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).mp4 15.5 MB
  • 2. 1-D Linear Regression Theory and Code/5. 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
  • 4. Practical machine learning issues/2. Interpreting the Weights.mp4 14.8 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/7. Determine how good the model is - r-squared.mp4 11.8 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
  • 2. 1-D Linear Regression Theory and Code/1. 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
  • 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/7. Probabilistic Interpretation of Squared Error.mp4 8.5 MB
  • 4. Practical machine learning issues/9. L2 Regularization - Code.mp4 8.5 MB
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. 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
  • 4. Practical machine learning issues/10. The Dummy Variable Trap.mp4 6.4 MB
  • 9. Appendix FAQ Finale/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/8. R-squared in code.mp4 4.7 MB
  • 2. 1-D Linear Regression Theory and Code/9. 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
  • 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
  • 3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.mp4 3.2 MB
  • 2. 1-D Linear Regression Theory and Code/12. R-squared Quiz 1.mp4 2.9 MB
  • 2. 1-D Linear Regression Theory and Code/6. Exercise Theory vs. Code.mp4 1.1 MB
  • 8/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32.5 kB
  • 8/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23.6 kB
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).srt 23.3 kB
  • 6. Setting Up Your Environment (FAQ by Student Request)/1. Anaconda Environment Setup.srt 20.6 kB
  • 2. 1-D Linear Regression Theory and Code/3. Define the model in 1-D, derive the solution (Updated Version).srt 16.9 kB
  • 8/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16.4 kB
  • 1. Welcome/3. Statistics vs. Machine Learning.srt 15.0 kB
  • 8/1. How to Succeed in this Course (Long Version).srt 14.9 kB
  • 6. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.8 kB
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.srt 14.5 kB
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).srt 13.6 kB
  • 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.srt 13.2 kB
  • 3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).srt 12.0 kB
  • 2. 1-D Linear Regression Theory and Code/4. Define the model in 1-D, derive the solution.srt 11.3 kB
  • 1. Welcome/1. Introduction and Outline.srt 11.0 kB
  • 4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.srt 9.4 kB
  • 4. Practical machine learning issues/17. Why Divide by Square Root of D.srt 8.9 kB
  • 1. Welcome/2. How to Succeed in this Course.srt 8.5 kB
  • 4. Practical machine learning issues/1. What do all these letters mean.srt 8.1 kB
  • 9. Appendix FAQ Finale/2. BONUS.srt 8.1 kB
  • 2. 1-D Linear Regression Theory and Code/11. Moore's Law Derivation.srt 7.8 kB
  • 2. 1-D Linear Regression Theory and Code/10. Demonstrating Moore's Law in Code.srt 7.1 kB
  • 4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.srt 6.6 kB
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.srt 6.2 kB
  • 2. 1-D Linear Regression Theory and Code/1. What is machine learning How does linear regression play a role.srt 6.0 kB
  • 5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.srt 5.8 kB
  • 2. 1-D Linear Regression Theory and Code/5. Coding the 1-D solution in Python.srt 5.8 kB
  • 4. Practical machine learning issues/8. L2 Regularization - Theory.srt 5.6 kB
  • 4. Practical machine learning issues/11. Gradient Descent Tutorial.srt 5.6 kB
  • 4. Practical machine learning issues/10. The Dummy Variable Trap.srt 5.6 kB
  • 3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.srt 5.6 kB
  • 5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.srt 5.5 kB
  • 3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.srt 5.3 kB
  • 3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).srt 5.1 kB
  • 4. Practical machine learning issues/5. Categorical inputs.srt 4.9 kB
  • 2. 1-D Linear Regression Theory and Code/13. Suggestion Box.srt 4.8 kB
  • 2. 1-D Linear Regression Theory and Code/7. Determine how good the model is - r-squared.srt 4.8 kB
  • 4. Practical machine learning issues/2. Interpreting the Weights.srt 4.4 kB
  • 4. Practical machine learning issues/16. L1 vs L2 Regularization.srt 4.4 kB
  • 4. Practical machine learning issues/14. L1 Regularization - Theory.srt 4.2 kB
  • 2. 1-D Linear Regression Theory and Code/9. Introduction to Moore's Law Problem.srt 3.8 kB
  • 9. Appendix FAQ Finale/1. What is the Appendix.srt 3.8 kB
  • 4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.srt 3.7 kB
  • 4. Practical machine learning issues/15. L1 Regularization - Code.srt 3.6 kB
  • 4. Practical machine learning issues/9. L2 Regularization - Code.srt 3.5 kB
  • 4. Practical machine learning issues/12. Gradient Descent for Linear Regression.srt 3.2 kB
  • 4. Practical machine learning issues/3. Generalization error, train and test sets.srt 2.9 kB
  • 3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.srt 2.8 kB
  • 4. Practical machine learning issues/6. One-Hot Encoding Quiz.srt 2.5 kB
  • 2. 1-D Linear Regression Theory and Code/12. R-squared Quiz 1.srt 2.3 kB
  • 3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.srt 2.1 kB
  • 2. 1-D Linear Regression Theory and Code/8. R-squared in code.srt 1.8 kB
  • 2. 1-D Linear Regression Theory and Code/6. Exercise Theory vs. Code.srt 1.6 kB
  • 2. 1-D Linear Regression Theory and Code/2. What can linear regression be used for.html 150 Bytes
  • 1. Welcome/[Tutorialsplanet.NET].url 128 Bytes
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/[Tutorialsplanet.NET].url 128 Bytes
  • [Tutorialsplanet.NET].url 128 Bytes

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