| 1. Anaconda Environment Setup.mp4 | 186.3 MB | ||
| 1. Anaconda Environment Setup.srt | 20.1 KB | ||
| 1. BONUS Sentiment Analysis.mp4 | 11.4 MB | ||
| 1. BONUS Sentiment Analysis.srt | 6.4 KB | ||
| 1. Facial Expression Recognition Project Introduction.mp4 | 9.8 MB | ||
| 1. Facial Expression Recognition Project Introduction.srt | 6.5 KB | ||
| 1. Gradient Descent Tutorial.mp4 | 22.8 MB | ||
| 1. Gradient Descent Tutorial.srt | 5.5 KB | ||
| 1. How to Succeed in this Course (Long Version).mp4 | 13 MB | ||
| 1. How to Succeed in this Course (Long Version).srt | 14.7 KB | ||
| 1. How to Uncompress a .tar.gz file.mp4 | 5.4 MB | ||
| 1. How to Uncompress a .tar.gz file.srt | 4.2 KB | ||
| 1. Introduction and Outline.mp4 | 39.4 MB | ||
| 1. Introduction and Outline.srt | 10.6 KB | ||
| 1. Linear Classification.mp4 | 7.5 MB | ||
| 1. Linear Classification.srt | 5.2 KB | ||
| 1. Practical Section Introduction.mp4 | 4.7 MB | ||
| 1. Practical Section Introduction.srt | 3.5 KB | ||
| 1. Training Section Introduction.mp4 | 2.8 MB | ||
| 1. Training Section Introduction.srt | 2 KB | ||
| 1. What is the Appendix.mp4 | 5.5 MB | ||
| 1. What is the Appendix.srt | 3.7 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 | ||
| 10. Suggestion Box.mp4 | 16.1 MB | ||
| 10. Suggestion Box.srt | 4.7 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 | ||
| 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 | ||
| 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 | ||
| 2. BONUS.mp4 | 37.8 MB | ||
| 2. BONUS.srt | 37.8 MB | ||
| 2. Biological inspiration - the neuron.mp4 | 9.4 MB | ||
| 2. Biological inspiration - the neuron.srt | 4.4 KB | ||
| 2. Facial Expression Recognition Problem Description.mp4 | 21.4 MB | ||
| 2. Facial Expression Recognition Problem Description.srt | 16 KB | ||
| 2. How to Code by Yourself (part 1).mp4 | 24.5 MB | ||
| 2. How to Code by Yourself (part 1).srt | 22.8 KB | ||
| 2. How to Succeed in this Course.mp4 | 43.8 MB | ||
| 2. How to Succeed in this Course.srt | 8.3 KB | ||
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 | 43.9 MB | ||
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt | 14.5 KB | ||
| 2. Interpreting the Weights.mp4 | 6.3 MB | ||
| 2. Interpreting the Weights.srt | 4.7 KB | ||
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 | 39 MB | ||
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt | 31.8 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 | ||
| 3. How to Code by Yourself (part 2).mp4 | 14.8 MB | ||
| 3. How to Code by Yourself (part 2).srt | 13.3 KB | ||
| 3. L2 Regularization - Theory.mp4 | 14.7 MB | ||
| 3. L2 Regularization - Theory.srt | 11.5 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 KB | ||
| 3. Statistics vs. Machine Learning.mp4 | 55.6 MB | ||
| 3. Statistics vs. Machine Learning.srt | 14.7 KB | ||
| 3. The class imbalance problem.mp4 | 10.1 MB | ||
| 3. The class imbalance problem.srt | 8 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. 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 | ||
| 4. L2 Regularization - Code.mp4 | 4.5 MB | ||
| 4. L2 Regularization - Code.srt | 1.6 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 KB | ||
| 4. Proof that using Jupyter Notebook is the same as not using it.mp4 | 78.3 MB | ||
| 4. Proof that using Jupyter Notebook is the same as not using it.srt | 14.1 KB | ||
| 4. Review of the classification problem.mp4 | 3 MB | ||
| 4. Review of the classification problem.srt | 2.2 KB | ||
| 4. The cross-entropy error function - Theory.mp4 | 4.5 MB | ||
| 4. The cross-entropy error function - Theory.srt | 4.4 KB | ||
| 4. Utilities walkthrough.mp4 | 13.5 MB | ||
| 4. Utilities walkthrough.srt | 5.8 KB | ||
| 5. Facial Expression Recognition in Code.mp4 | 24 MB | ||
| 5. Facial Expression Recognition in Code.srt | 8.1 KB | ||
| 5. Interpretation of Logistic Regression Output.mp4 | 27.9 MB | ||
| 5. Interpretation of Logistic Regression Output.srt | 6.4 KB | ||
| 5. Introduction to the E-Commerce Course Project.mp4 | 14.8 MB | ||
| 5. Introduction to the E-Commerce Course Project.srt | 14 KB | ||
| 5. L1 Regularization - Theory.mp4 | 4.4 MB | ||
| 5. L1 Regularization - Theory.srt | 3.7 KB | ||
| 5. Python 2 vs Python 3.mp4 | 7.8 MB | ||
| 5. Python 2 vs Python 3.srt | 6.1 KB | ||
| 5. The cross-entropy error function - Code.mp4 | 9.1 MB | ||
| 5. The cross-entropy error function - Code.srt | 3.9 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 | ||
| 6. Easy first quiz.html | 102.4 B | ||
| 6. Facial Expression Recognition Project Summary.mp4 | 2.9 MB | ||
| 6. Facial Expression Recognition Project Summary.srt | 1.6 KB | ||
| 6. L1 Regularization - Code.mp4 | 12 MB | ||
| 6. L1 Regularization - Code.srt | 4.6 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. E-Commerce Course Project Making Predictions.mp4 | 5.7 MB | ||
| 7. E-Commerce Course Project Making Predictions.srt | 3 KB | ||
| 7. L1 vs L2 Regularization.mp4 | 4.8 MB | ||
| 7. L1 vs L2 Regularization.srt | 4.3 KB | ||
| 7. Maximizing the likelihood.mp4 | 25.2 MB | ||
| 7. Maximizing the likelihood.srt | 4 KB | ||
| 8. Feedforward Quiz.mp4 | 2.3 MB | ||
| 8. Feedforward Quiz.srt | 1.7 KB | ||
| 8. The donut problem.mp4 | 24.7 MB | ||
| 8. The donut problem.srt | 7.4 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. Prediction Section Summary.mp4 | 2.2 MB | ||
| 9. Prediction Section Summary.srt | 1.5 KB | ||
| 9. The XOR problem.mp4 | 14.2 MB | ||
| 9. The XOR problem.srt | 6.1 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 | ||
| [Tutorialsplanet.NET].url | 102.4 B | ||
| ▲ 122 total files | |||
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
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| 582.4 MB | freecoursewb | 7 months | 0 | 0 | |
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Udemy - AWS Networking Deep-Dive Crash Course - Master VPC Essentials Posted by
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