Udemy - Deep Learning Prerequisites: Logistic Regression in Python

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  • LanguageEnglish
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  • Last checkedNov. 10th '21
  • Date uploadedNov. 07th '21
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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.

This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.
This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!
For more Udemy Courses: https://tutorialsplanet.net

Files:

[Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Logistic Regression in Python 1. Start Here
  • 1. Introduction and Outline.mp4 (39.4 MB)
  • 1. Introduction and Outline.srt (10.6 KB)
  • 2. How to Succeed in this Course.mp4 (43.8 MB)
  • 2. How to Succeed in this Course.srt (8.3 KB)
  • 3. Statistics vs. Machine Learning.mp4 (55.6 MB)
  • 3. Statistics vs. Machine Learning.srt (14.7 KB)
  • 4. Review of the classification problem.mp4 (3.0 MB)
  • 4. Review of the classification problem.srt (2.2 KB)
  • 5. Introduction to the E-Commerce Course Project.mp4 (14.8 MB)
  • 5. Introduction to the E-Commerce Course Project.srt (14.0 KB)
  • 6. Easy first quiz.html (0.1 KB)
  • [Tutorialsplanet.NET].url (0.1 KB)
10
  • 1. How to Succeed in this Course (Long Version).mp4 (13.0 MB)
  • 1. How to Succeed in this Course (Long Version).srt (14.7 KB)
  • 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 (39.0 MB)
  • 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt (31.8 KB)
  • 3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 (29.3 MB)
  • 3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt (16.0 KB)
  • 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 (37.6 MB)
  • 4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt (23.0 KB)
11. Appendix FAQ Finale
  • 1. What is the Appendix.mp4 (5.5 MB)
  • 1. What is the Appendix.srt (3.7 KB)
  • 2. BONUS.mp4 (37.8 MB)
  • 2. BONUS.srt (37.8 MB)
2
  • 1. Linear Classification.mp4 (7.5 MB)
  • 1. Linear Classification.srt (5.2 KB)
  • 10. Suggestion Box.mp4 (16.1 MB)
  • 10. Suggestion Box.srt (4.7 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 (3.9 KB)
  • 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 (7.2 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 (2.6 KB)
  • 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 (3.7 KB)
  • 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 Exercises + how to get good at this.mp4 (5.3 MB)
  • 2. 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)

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