Udemy - Deep Learning Prerequisites Logistic Regression in Python [GC]

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[GigaCourse.com] Udemy - Deep Learning Prerequisites Logistic Regression in Python 1. Start Here
  • 1. Introduction and Outline.mp4 (46.9 MB)
  • 1. Introduction and Outline.srt (5.3 KB)
  • 2. How to Succeed in this Course.mp4 (6.4 MB)
  • 2. How to Succeed in this Course.srt (4.0 KB)
  • 3. Review of the classification problem.mp4 (3.0 MB)
  • 3. Review of the classification problem.srt (2.2 KB)
  • 4. Introduction to the E-Commerce Course Project.mp4 (14.8 MB)
  • 4. Introduction to the E-Commerce Course Project.srt (7.6 MB)
  • 5. Easy first quiz.html (0.1 KB)
2. Basics What is linear classification What's the relation to neural networks
  • 1. Linear Classification.mp4 (7.6 MB)
  • 1. Linear Classification.srt (5.2 KB)
  • 2. Biological inspiration - the neuron.mp4 (9.4 MB)
  • 2. Biological inspiration - the neuron.srt (4.4 KB)
  • 3. How do we calculate the output of a neuron logistic classifier - Theory.mp4 (15.2 MB)
  • 3. How do we calculate the output of a neuron logistic classifier - Theory.srt (80.2 MB)
  • 4. How do we calculate the output of a neuron logistic classifier - Code.mp4 (5.8 MB)
  • 4. How do we calculate the output of a neuron logistic classifier - Code.srt (4.5 KB)
  • 5. Interpretation of Logistic Regression Output.mp4 (27.9 MB)
  • 5. Interpretation of Logistic Regression Output.srt (6.4 KB)
  • 6. E-Commerce Course Project Pre-Processing the Data.mp4 (11.2 MB)
  • 6. E-Commerce Course Project Pre-Processing the Data.srt (5.1 KB)
  • 7. E-Commerce Course Project Making Predictions.mp4 (5.7 MB)
  • 7. E-Commerce Course Project Making Predictions.srt (3.0 KB)
  • 8. Feedforward Quiz.mp4 (2.3 MB)
  • 8. Feedforward Quiz.srt (1.7 KB)
  • 9. Prediction Section Summary.mp4 (2.2 MB)
  • 9. Prediction Section Summary.srt (1.5 KB)
3. Solving for the optimal weights
  • 1. Training Section Introduction.mp4 (2.8 MB)
  • 1. Training Section Introduction.srt (2.0 KB)
  • 10. E-Commerce Course Project Training the Logistic Model.mp4 (17.1 MB)
  • 10. E-Commerce Course Project Training the Logistic Model.srt (5.3 KB)
  • 11. Training Section Summary.mp4 (3.4 MB)
  • 11. Training Section Summary.srt (2.6 KB)
  • 2. A closed-form solution to the Bayes classifier.mp4 (9.1 MB)
  • 2. A closed-form solution to the Bayes classifier.srt (7.3 KB)
  • 3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..mp4 (6.4 MB)
  • 3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..srt (5.2 KB)
  • 4. The cross-entropy error function - Theory.mp4 (4.5 MB)
  • 4. The cross-entropy error function - Theory.srt (4.4 KB)
  • 5. The cross-entropy error function - Code.mp4 (9.1 MB)
  • 5. The cross-entropy error function - Code.srt (3.9 KB)
  • 6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.mp4 (5.3 MB)
  • 6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.srt (2.3 KB)
  • 7. Maximizing the likelihood.mp4 (25.2 MB)
  • 7. Maximizing the likelihood.srt (4.0 KB)
  • 8. Updating the weights using gradient descent - Theory.mp4 (9.3 MB)
  • 8. Updating the weights using gradient descent - Theory.srt (8.1 KB)
  • 9. Updating the weights using gradient descent - Code.mp4 (7.3 MB)
  • 9. Updating the weights using gradient descent - Code.srt (2.5 KB)
4. Practical concerns
  • 1. Practical Section Introduction.mp4 (4.7 MB)
  • 1. Practical Section Introduction.srt (3.5 KB)
  • 10. Why Divide by Square Root of D.mp4 (23.5 MB)
  • 10. Why Divide by Square Root of D.srt (8.7 KB)
  • 11. Practical Section Summary.mp4 (3.4 MB)
  • 11. Practical Section Summary.srt (78.3 MB)
  • 2. Interpreting the Weights.mp4 (6.3 MB)
  • 2. Interpreting the Weights.srt (4.7 KB)
  • 3. L2 Regularization - Theory.mp4 (14.7 MB)
  • 3. L2 Regularization - Theory.srt (11.5 KB)
  • 4. L2 Regularization - Code.mp4 (4.5 MB)
  • 4. L2 Regularization - Code.srt (1.6 KB)
  • 5. L1 Regularization - Theory.mp4 (4.4 MB)
  • 5. L1 Regularization - Theory.srt (14.9 MB)
  • 6. L1 Regularization - Code.mp4 (12.0 MB)
  • 6. L1 Regularization - Code.srt (4.6 KB)
  • 7. L1 vs L2 Regularization.mp4 (4.8 MB)
  • 7. L1 vs L2 Regularization.srt (4.3 KB)
  • 8. The donut problem.mp4 (24.7 MB)
  • 8. The donut problem.srt (7.4 KB)
  • 9. The XOR problem.mp4 (14.2 MB)
  • 9. The XOR problem.srt (6.1 KB)
5. Checkpoint and applications How to make sure you know your stuff
  • 1. BONUS Sentiment Analysis.mp4 (11.4 MB)
  • 1. BONUS Sentiment Analysis.srt (6.4 KB)
  • 2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 (4.0 MB)
  • 2. BONUS Where to get Udemy coupons and FREE deep learning material.srt (3.4 KB)
  • 3. BONUS Exercises + how to get good at this.mp4 (5.3 MB)
  • 3. BONUS Exercises + how to get good at this.srt (3.8 KB)
6. Project Facial Expression Recognition
  • 1. Facial Expression Recognition Project Introduction.mp4 (9.8 MB)
  • 1. Facial Expression Recognition Project Introduction.srt (6.5 KB)
  • 2. Facial Expression Recognition Problem Description.mp4 (21.4 MB)
  • 2. Facial Expression Recognition Problem Description.srt (16.0 KB)
  • 3. The class imbalance problem.mp4 (10.1 MB)
  • 3. The class imbalance problem.srt (8.0 KB)
  • 4. Utilities walkthrough.mp4 (13.5 MB)
  • 4. Utilities walkthrough.srt (5.8 KB)
  • 5. Facial Expression Recognition in Code.mp4 (24.0 MB)
  • 5. Facial Expression Recognition in Code.srt (8.1 KB)
  • 6. Facial Expression Recognition Project Summary.mp4 (2.9 MB)
  • 6. Facial Expression Recognition Project Summary.srt (1.7 KB)
7. Appendix FAQ
  • 1. What is the Appendix.mp4 (5.5 MB)
  • 1. What is the Appendix.srt (3.8 KB)
  • 10. Proof that using Jupyter Notebook is the same as not using it.mp4 (78.3 MB)
  • 10. Proof that using Jupyter Notebook is the same as not using it.srt (78.3 MB)
  • 11. Python 2 vs Python 3.mp4 (7.8 MB)
  • 11. Python 2 vs Python 3.srt (6.6 KB)
  • 12. What order should I take your courses in (part 1).mp4 (29.3 MB)

Description

Udemy - Deep Learning Prerequisites Logistic Regression in Python

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python.

For more Udemy Courses: https://gigacourse.com



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Udemy - Deep Learning Prerequisites Logistic Regression in Python [GC]

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1.3 GB
seeders:6
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Udemy - Deep Learning Prerequisites Logistic Regression in Python [GC]

Torrent hash: F1121C4C04E049A209C8BC475237206093946943