种子简介

种子名称： [Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Linear Regression in Python
文件类型： 视频
文件数目： 54个文件
文件大小： 1.11 GB
收录时间： 2021-7-13 16:39
已经下载： 3次
资源热度： 255
最近下载： 2024-6-29 02:09

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:32b7bb017c4d88324beb30985a2197892d23a92e&dn=[Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Linear Regression in Python 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

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

1. Welcome/1. Welcome.mp449.68MB

1. Welcome/2. Introduction and Outline.mp46.34MB

1. Welcome/3. What is machine learning How does linear regression play a role.mp48.43MB

1. Welcome/4. Anyone Can Succeed in this Course.mp483.98MB

1. Welcome/5. Statistics vs. Machine Learning.mp455.52MB

2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp419.34MB

2. 1-D Linear Regression Theory and Code/10. R-squared Quiz 1.mp42.8MB

2. 1-D Linear Regression Theory and Code/11. Suggestion Box.mp416.08MB

2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp424.66MB

2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.mp414.43MB

2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.mp41.05MB

2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp411.3MB

2. 1-D Linear Regression Theory and Code/6. R-squared in code.mp44.5MB

2. 1-D Linear Regression Theory and Code/7. Introduction to Moore's Law Problem.mp44.41MB

2. 1-D Linear Regression Theory and Code/8. Demonstrating Moore's Law in Code.mp417.51MB

2. 1-D Linear Regression Theory and Code/9. Moore's Law Derivation.mp420.18MB

3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).mp414.44MB

3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp436.08MB

3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.mp43.1MB

3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp414.92MB

3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).mp416.4MB

3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.mp412.34MB

3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.mp43.5MB

4. Practical machine learning issues/1. What do all these letters mean.mp49.63MB

4. Practical machine learning issues/10. The Dummy Variable Trap.mp46.07MB

4. Practical machine learning issues/11. Gradient Descent Tutorial.mp422.8MB

4. Practical machine learning issues/12. Gradient Descent for Linear Regression.mp43.51MB

4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.mp48.5MB

4. Practical machine learning issues/14. L1 Regularization - Theory.mp44.66MB

4. Practical machine learning issues/15. L1 Regularization - Code.mp48.26MB

4. Practical machine learning issues/16. L1 vs L2 Regularization.mp44.8MB

4. Practical machine learning issues/17. Why Divide by Square Root of D.mp423.49MB

4. Practical machine learning issues/2. Interpreting the Weights.mp414.15MB

4. Practical machine learning issues/3. Generalization error, train and test sets.mp44.39MB

4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp417.25MB

4. Practical machine learning issues/5. Categorical inputs.mp48.18MB

4. Practical machine learning issues/6. One-Hot Encoding Quiz.mp43.78MB

4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.mp48.13MB

4. Practical machine learning issues/8. L2 Regularization - Theory.mp46.65MB

4. Practical machine learning issues/9. L2 Regularization - Code.mp48.08MB

5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.mp48.13MB

5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.mp47.16MB

6. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4186.29MB

6. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB

7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp424.54MB

7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).mp414.81MB

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.mp478.28MB

7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.mp47.83MB

8/1. How to Succeed in this Course (Long Version).mp418.32MB

8/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB

8/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp429.32MB

8/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp437.62MB

9. Appendix FAQ Finale/1. What is the Appendix.mp45.45MB

9. Appendix FAQ Finale/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp437.83MB