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种子名称： [GigaCourse.com] Udemy - Deep Learning Prerequisites Logistic Regression in Python
文件类型： 视频
文件数目： 58个文件
文件大小： 1023.64 MB
收录时间： 2021-5-2 05:49
已经下载： 3次
资源热度： 224
最近下载： 2024-12-29 00:06

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[GigaCourse.com] Udemy - Deep Learning Prerequisites Logistic Regression in Python.torrent

1. Start Here/1. Introduction and Outline.mp446.92MB

1. Start Here/2. How to Succeed in this Course.mp46.41MB

1. Start Here/3. Review of the classification problem.mp42.97MB

1. Start Here/4. Introduction to the E-Commerce Course Project.mp414.79MB

2. Basics What is linear classification What's the relation to neural networks/1. Linear Classification.mp47.55MB

2. Basics What is linear classification What's the relation to neural networks/2. Biological inspiration - the neuron.mp49.39MB

2. Basics What is linear classification What's the relation to neural networks/3. How do we calculate the output of a neuron logistic classifier - Theory.mp415.22MB

2. Basics What is linear classification What's the relation to neural networks/4. How do we calculate the output of a neuron logistic classifier - Code.mp45.82MB

2. Basics What is linear classification What's the relation to neural networks/5. Interpretation of Logistic Regression Output.mp427.89MB

2. Basics What is linear classification What's the relation to neural networks/6. E-Commerce Course Project Pre-Processing the Data.mp411.17MB

2. Basics What is linear classification What's the relation to neural networks/7. E-Commerce Course Project Making Predictions.mp45.7MB

2. Basics What is linear classification What's the relation to neural networks/8. Feedforward Quiz.mp42.27MB

2. Basics What is linear classification What's the relation to neural networks/9. Prediction Section Summary.mp42.22MB

3. Solving for the optimal weights/1. Training Section Introduction.mp42.81MB

3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.mp417.06MB

3. Solving for the optimal weights/11. Training Section Summary.mp43.39MB

3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.mp49.11MB

3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..mp46.37MB

3. Solving for the optimal weights/4. The cross-entropy error function - Theory.mp44.5MB

3. Solving for the optimal weights/5. The cross-entropy error function - Code.mp49.1MB

3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.mp45.27MB

3. Solving for the optimal weights/7. Maximizing the likelihood.mp425.22MB

3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.mp49.35MB

3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.mp47.26MB

4. Practical concerns/1. Practical Section Introduction.mp44.73MB

4. Practical concerns/10. Why Divide by Square Root of D.mp423.48MB

4. Practical concerns/11. Practical Section Summary.mp43.41MB

4. Practical concerns/2. Interpreting the Weights.mp46.34MB

4. Practical concerns/3. L2 Regularization - Theory.mp414.7MB

4. Practical concerns/4. L2 Regularization - Code.mp44.47MB

4. Practical concerns/5. L1 Regularization - Theory.mp44.42MB

4. Practical concerns/6. L1 Regularization - Code.mp412.01MB

4. Practical concerns/7. L1 vs L2 Regularization.mp44.8MB

4. Practical concerns/8. The donut problem.mp424.69MB

4. Practical concerns/9. The XOR problem.mp414.21MB

5. Checkpoint and applications How to make sure you know your stuff/1. BONUS Sentiment Analysis.mp411.41MB

5. Checkpoint and applications How to make sure you know your stuff/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp44.03MB

5. Checkpoint and applications How to make sure you know your stuff/3. BONUS Exercises + how to get good at this.mp45.26MB

6. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.mp49.82MB

6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp421.44MB

6. Project Facial Expression Recognition/3. The class imbalance problem.mp410.11MB

6. Project Facial Expression Recognition/4. Utilities walkthrough.mp413.49MB

6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp424.05MB

6. Project Facial Expression Recognition/6. Facial Expression Recognition Project Summary.mp42.92MB

7. Appendix FAQ/1. What is the Appendix.mp45.45MB

7. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.mp478.28MB

7. Appendix FAQ/11. Python 2 vs Python 3.mp47.84MB

7. Appendix FAQ/12. What order should I take your courses in (part 1).mp429.32MB

7. Appendix FAQ/13. What order should I take your courses in (part 2).mp437.62MB

7. Appendix FAQ/14. BONUS Where to get discount coupons and FREE deep learning material.mp437.83MB

7. Appendix FAQ/2. Gradient Descent Tutorial.mp422.82MB

7. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4186.28MB

7. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB

7. Appendix FAQ/5. How to Code by Yourself (part 1).mp424.53MB

7. Appendix FAQ/6. How to Code by Yourself (part 2).mp414.8MB

7. Appendix FAQ/7. How to Uncompress a .tar.gz file.mp45.43MB

7. Appendix FAQ/8. How to Succeed in this Course (Long Version).mp412.99MB

7. Appendix FAQ/9. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.96MB