Skip to content

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

Sign up to create lists
Report [GigaCourse com] Udemy - Deep Learning Prerequisites Logistic Regression in Python
Unverified source. This magnet is from an unverified source. The content may be unsafe or mislabeled. Proceed with caution.
Title: UdemyGroup: NOGRPSource: Udemy
Info Hash
F1121C4C04E049A209C8BC475237206093946943
Source
Unverified
Total Size
1.25 GB
Total Files
100
Seeders
0
Leechers
1
Health
Score
1
Type
Bookware

File List

FileSize
1. Start Here/1. Introduction and Outline.mp446.92 MB
1. Start Here/1. Introduction and Outline.srt5.31 KB
1. Start Here/2. How to Succeed in this Course.mp46.41 MB
1. Start Here/2. How to Succeed in this Course.srt4.04 KB
1. Start Here/3. Review of the classification problem.mp42.97 MB
1. Start Here/3. Review of the classification problem.srt2.22 KB
1. Start Here/4. Introduction to the E-Commerce Course Project.mp414.79 MB
1. Start Here/4. Introduction to the E-Commerce Course Project.srt7.63 MB
1. Start Here/5. Easy first quiz.html152 B
2. Basics What is linear classification What's the relation to neural networks/1. Linear Classification.mp47.55 MB
2. Basics What is linear classification What's the relation to neural networks/1. Linear Classification.srt5.17 KB
2. Basics What is linear classification What's the relation to neural networks/2. Biological inspiration - the neuron.mp49.39 MB
2. Basics What is linear classification What's the relation to neural networks/2. Biological inspiration - the neuron.srt4.37 KB
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.mp415.22 MB
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.srt80.18 MB
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.mp45.82 MB
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.srt4.49 KB
2. Basics What is linear classification What's the relation to neural networks/5. Interpretation of Logistic Regression Output.mp427.89 MB
2. Basics What is linear classification What's the relation to neural networks/5. Interpretation of Logistic Regression Output.srt6.38 KB
2. Basics What is linear classification What's the relation to neural networks/6. E-Commerce Course Project Pre-Processing the Data.mp411.17 MB
2. Basics What is linear classification What's the relation to neural networks/6. E-Commerce Course Project Pre-Processing the Data.srt5.13 KB
2. Basics What is linear classification What's the relation to neural networks/7. E-Commerce Course Project Making Predictions.mp45.7 MB
2. Basics What is linear classification What's the relation to neural networks/7. E-Commerce Course Project Making Predictions.srt3 KB
2. Basics What is linear classification What's the relation to neural networks/8. Feedforward Quiz.mp42.27 MB
2. Basics What is linear classification What's the relation to neural networks/8. Feedforward Quiz.srt1.69 KB
2. Basics What is linear classification What's the relation to neural networks/9. Prediction Section Summary.mp42.22 MB
2. Basics What is linear classification What's the relation to neural networks/9. Prediction Section Summary.srt1.47 KB
3. Solving for the optimal weights/1. Training Section Introduction.mp42.81 MB
3. Solving for the optimal weights/1. Training Section Introduction.srt2.05 KB
3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.mp417.06 MB
3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.srt5.26 KB
3. Solving for the optimal weights/11. Training Section Summary.mp43.39 MB
3. Solving for the optimal weights/11. Training Section Summary.srt2.59 KB
3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.mp49.11 MB
3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.srt7.33 KB
3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..mp46.37 MB
3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..srt5.21 KB
3. Solving for the optimal weights/4. The cross-entropy error function - Theory.mp44.5 MB
3. Solving for the optimal weights/4. The cross-entropy error function - Theory.srt4.39 KB
3. Solving for the optimal weights/5. The cross-entropy error function - Code.mp49.1 MB
3. Solving for the optimal weights/5. The cross-entropy error function - Code.srt3.94 KB
3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.mp45.27 MB
3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.srt2.29 KB
3. Solving for the optimal weights/7. Maximizing the likelihood.mp425.22 MB
3. Solving for the optimal weights/7. Maximizing the likelihood.srt3.96 KB
3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.mp49.35 MB
3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.srt8.15 KB
3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.mp47.26 MB
3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.srt2.49 KB
4. Practical concerns/1. Practical Section Introduction.mp44.73 MB
4. Practical concerns/1. Practical Section Introduction.srt3.49 KB
4. Practical concerns/10. Why Divide by Square Root of D.mp423.48 MB
4. Practical concerns/10. Why Divide by Square Root of D.srt8.68 KB
4. Practical concerns/11. Practical Section Summary.mp43.41 MB
4. Practical concerns/11. Practical Section Summary.srt78.27 MB
4. Practical concerns/2. Interpreting the Weights.mp46.34 MB
4. Practical concerns/2. Interpreting the Weights.srt4.72 KB
4. Practical concerns/3. L2 Regularization - Theory.mp414.7 MB
4. Practical concerns/3. L2 Regularization - Theory.srt11.5 KB
4. Practical concerns/4. L2 Regularization - Code.mp44.47 MB
4. Practical concerns/4. L2 Regularization - Code.srt1.65 KB
4. Practical concerns/5. L1 Regularization - Theory.mp44.42 MB
4. Practical concerns/5. L1 Regularization - Theory.srt14.94 MB
4. Practical concerns/6. L1 Regularization - Code.mp412.01 MB
4. Practical concerns/6. L1 Regularization - Code.srt4.62 KB
4. Practical concerns/7. L1 vs L2 Regularization.mp44.8 MB
4. Practical concerns/7. L1 vs L2 Regularization.srt4.25 KB
4. Practical concerns/8. The donut problem.mp424.69 MB
4. Practical concerns/8. The donut problem.srt7.35 KB
4. Practical concerns/9. The XOR problem.mp414.21 MB
4. Practical concerns/9. The XOR problem.srt6.09 KB
5. Checkpoint and applications How to make sure you know your stuff/1. BONUS Sentiment Analysis.mp411.41 MB
5. Checkpoint and applications How to make sure you know your stuff/1. BONUS Sentiment Analysis.srt6.43 KB
5. Checkpoint and applications How to make sure you know your stuff/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp44.03 MB
5. Checkpoint and applications How to make sure you know your stuff/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt3.42 KB
5. Checkpoint and applications How to make sure you know your stuff/3. BONUS Exercises + how to get good at this.mp45.26 MB
5. Checkpoint and applications How to make sure you know your stuff/3. BONUS Exercises + how to get good at this.srt3.82 KB
6. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.mp49.82 MB
6. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.srt6.47 KB
6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp421.44 MB
6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.srt16.01 KB
6. Project Facial Expression Recognition/3. The class imbalance problem.mp410.11 MB
6. Project Facial Expression Recognition/3. The class imbalance problem.srt7.96 KB
6. Project Facial Expression Recognition/4. Utilities walkthrough.mp413.49 MB
6. Project Facial Expression Recognition/4. Utilities walkthrough.srt5.84 KB
6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp424.05 MB
6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.srt8.1 KB
6. Project Facial Expression Recognition/6. Facial Expression Recognition Project Summary.mp42.92 MB
6. Project Facial Expression Recognition/6. Facial Expression Recognition Project Summary.srt1.74 KB
7. Appendix FAQ/1. What is the Appendix.mp45.45 MB
7. Appendix FAQ/1. What is the Appendix.srt3.82 KB
7. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.mp478.28 MB
7. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.srt78.3 MB
7. Appendix FAQ/11. Python 2 vs Python 3.mp47.84 MB
7. Appendix FAQ/11. Python 2 vs Python 3.srt6.65 KB
7. Appendix FAQ/12. What order should I take your courses in (part 1).mp429.32 MB
7. Appendix FAQ/12. What order should I take your courses in (part 1).srt17.09 KB
7. Appendix FAQ/13. What order should I take your courses in (part 2).mp437.62 MB
7. Appendix FAQ/13. What order should I take your courses in (part 2).srt25.1 KB
7. Appendix FAQ/14. BONUS Where to get discount coupons and FREE deep learning material.mp437.83 MB

Trackers

No trackers found.