deep-learning-prerequisites-linear-regression-in-python

磁力链接/BT种子名称

deep-learning-prerequisites-linear-regression-in-python

磁力链接/BT种子简介

种子哈希：fe6f58e344c2f4a2c6886611184f7b475e58e659
文件大小： 1.12G
已经下载：12次
下载速度：极快
收录时间：2025-07-20
最近下载：2025-10-06

地址随时变，回家记住路

小野猫.com黑猫警长.com哆啦a猫.com御猫.com科目三.com猫哭老鼠.com女猫.com☜☜☜找最新地址请保存左面网址

磁力链接

magnet:?xt=urn:btih:FE6F58E344C2F4A2C6886611184F7B475E58E659
推荐使用PIKPAK网盘下载资源，PIKPAK是目前最好用网盘，10T超大空间，不和谐任何资源，支持无限次数离线下载，视频在线观看

下载BT种子文件

磁力链接 迅雷下载 在线观看 世界之窗 含羞草 极乐禁地 91视频 51品茶 逼哩逼哩 萝莉岛 欲漫涩 91短视频 成人快手 抖阴破解版 ai色色 pilipili 草榴社区 哆哔涩漫 好色先生 疯马秀 TikTok成人版 悠悠禁区 波多 听泉鉴鲍 xvideo 外网天堂 PornHub 抖音Max 呦乐园 拔萝卜 糖心视频 麻豆Vlog 暗网Xvideo

资源截图

API Integration

最近搜索

文件列表

  • 6. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4 195.3 MB
  • 1. Welcome/4. Anyone Can Succeed in this Course.mp4 88.1 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/5. Statistics vs. Machine Learning.mp4 58.2 MB
  • 1. Welcome/1. Welcome.mp4 52.1 MB
  • 6. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 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 Where to get Udemy coupons and FREE deep learning material.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/2. 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/9. Moore's Law Derivation.mp4 21.2 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
  • 8/1. How to Succeed in this Course (Long Version).mp4 19.2 MB
  • 2. 1-D Linear Regression Theory and Code/8. Demonstrating Moore's Law in Code.mp4 18.4 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/11. 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
  • 3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).mp4 15.1 MB
  • 2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.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/5. 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
  • 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
  • 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
  • 1. Welcome/2. Introduction and Outline.mp4 6.6 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/6. R-squared in code.mp4 4.7 MB
  • 2. 1-D Linear Regression Theory and Code/7. 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 3.3 MB
  • 2. 1-D Linear Regression Theory and Code/10. 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
  • Deep Learning Prerequisites Linear Regression in Python.torrent 37.2 kB
  • 8/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 34.6 kB
  • 8/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 25.8 kB
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).srt 24.8 kB
  • 6. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.srt 22.2 kB
  • Deep Learning Prerequisites Linear Regression in Python_torrent.txt 19.6 kB
  • 1. Welcome/4. Anyone Can Succeed in this Course.srt 19.5 kB
  • 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).srt 18.1 kB
  • 8/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 17.6 kB
  • 1. Welcome/5. Statistics vs. Machine Learning.srt 16.4 kB
  • 6. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 15.9 kB
  • 8/1. How to Succeed in this Course (Long Version).srt 15.6 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 15.4 kB
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).srt 14.3 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 12.8 kB
  • 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.srt 11.3 kB
  • deep-learning-prerequisites-linear-regression-in-python_meta.sqlite 11.3 kB
  • 4. Practical machine learning issues/17. Why Divide by Square Root of D.srt 9.6 kB
  • 4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.srt 9.4 kB
  • 4. Practical machine learning issues/1. What do all these letters mean.srt 8.7 kB
  • 9. Appendix FAQ Finale/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 8.6 kB
  • 2. 1-D Linear Regression Theory and Code/9. Moore's Law Derivation.srt 8.3 kB
  • 2. 1-D Linear Regression Theory and Code/8. Demonstrating Moore's Law in Code.srt 7.1 kB
  • 4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.srt 6.9 kB
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.srt 6.7 kB
  • 4. Practical machine learning issues/8. L2 Regularization - Theory.srt 6.1 kB
  • 4. Practical machine learning issues/11. Gradient Descent Tutorial.srt 6.1 kB
  • 4. Practical machine learning issues/10. The Dummy Variable Trap.srt 6.0 kB
  • 2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.srt 6.0 kB
  • 1. Welcome/2. Introduction and Outline.srt 6.0 kB
  • 1. Welcome/3. 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
  • 3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.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/5. Polynomial regression - extending linear regression (with Python code).srt 5.4 kB
  • 2. 1-D Linear Regression Theory and Code/11. Suggestion Box.srt 5.0 kB
  • 4. Practical machine learning issues/5. Categorical inputs.srt 4.9 kB
  • 1. Welcome/1. Welcome.srt 4.9 kB
  • 2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.srt 4.8 kB
  • 4. Practical machine learning issues/2. Interpreting the Weights.srt 4.7 kB
  • 4. Practical machine learning issues/14. L1 Regularization - Theory.srt 4.6 kB
  • 4. Practical machine learning issues/16. L1 vs L2 Regularization.srt 4.6 kB
  • 9. Appendix FAQ Finale/1. What is the Appendix.srt 4.0 kB
  • 4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.srt 3.9 kB
  • 4. Practical machine learning issues/15. L1 Regularization - Code.srt 3.9 kB
  • 2. 1-D Linear Regression Theory and Code/7. Introduction to Moore's Law Problem.srt 3.8 kB
  • 4. Practical machine learning issues/9. L2 Regularization - Code.srt 3.7 kB
  • 4. Practical machine learning issues/12. Gradient Descent for Linear Regression.srt 3.5 kB
  • 3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.srt 3.0 kB
  • 4. Practical machine learning issues/3. Generalization error, train and test sets.srt 2.9 kB
  • 4. Practical machine learning issues/6. One-Hot Encoding Quiz.srt 2.8 kB
  • 2. 1-D Linear Regression Theory and Code/10. R-squared Quiz 1.srt 2.4 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/6. R-squared in code.srt 1.9 kB
  • 2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.srt 1.7 kB
  • deep-learning-prerequisites-linear-regression-in-python_meta.xml 924 Bytes
  • 1. Welcome/6. What can linear regression be used for.html 150 Bytes
  • [Tutorialsplanet.NET].url 128 Bytes
  • 7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/[Tutorialsplanet.NET].url 128 Bytes
  • 4. Practical machine learning issues/[Tutorialsplanet.NET].url 128 Bytes
  • 1. Welcome/[Tutorialsplanet.NET].url 128 Bytes

温馨提示

本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！