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

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Udemy - Deep Learning Prerequisites Logistic Regression in Python [GC] (Size: 1.3 GB)
  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. Introduction and Outline.mp4 46.9 MB
  1. Introduction and Outline.srt 5.3 KB
  1. Linear Classification.mp4 7.6 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 3.8 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. 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
  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
  11. Python 2 vs Python 3.mp4 7.8 MB
  11. Python 2 vs Python 3.srt 6.6 KB
  11. Training Section Summary.mp4 3.4 MB
  11. Training Section Summary.srt 2.6 KB
  12. What order should I take your courses in (part 1).mp4 29.3 MB
  12. What order should I take your courses in (part 1).srt 17.1 KB
  13. What order should I take your courses in (part 2).mp4 37.6 MB
  13. What order should I take your courses in (part 2).srt 25.1 KB
  14. BONUS Where to get discount coupons and FREE deep learning material.mp4 37.8 MB
  14. BONUS Where to get discount coupons and FREE deep learning material.srt 8.4 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 Where to get Udemy coupons and FREE deep learning material.mp4 4 MB
  2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 3.4 KB
  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. Gradient Descent Tutorial.mp4 22.8 MB
  2. Gradient Descent Tutorial.srt 5.9 KB
  2. How to Succeed in this Course.mp4 6.4 MB
  2. How to Succeed in this Course.srt 4 KB
  2. Interpreting the Weights.mp4 6.3 MB
  2. Interpreting the Weights.srt 4.7 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
  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
  3. L2 Regularization - Theory.mp4 14.7 MB
  3. L2 Regularization - Theory.srt 11.5 KB
  3. Review of the classification problem.mp4 3 MB
  3. Review of the classification problem.srt 2.2 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
  3. Windows-Focused Environment Setup 2018.mp4 186.3 MB
  3. Windows-Focused Environment Setup 2018.srt 21.6 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. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
  4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 15.5 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
  4. L2 Regularization - Code.mp4 4.5 MB
  4. L2 Regularization - Code.srt 1.6 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. Easy first quiz.html 102.4 B
  5. Facial Expression Recognition in Code.mp4 24 MB
  5. Facial Expression Recognition in Code.srt 8.1 KB
  5. How to Code by Yourself (part 1).mp4 24.5 MB
  5. How to Code by Yourself (part 1).srt 24.3 KB
  5. Interpretation of Logistic Regression Output.mp4 27.9 MB
  5. Interpretation of Logistic Regression Output.srt 6.4 KB
  5. L1 Regularization - Theory.mp4 4.4 MB
  5. L1 Regularization - Theory.srt 14.9 MB
  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. Facial Expression Recognition Project Summary.mp4 2.9 MB
  6. Facial Expression Recognition Project Summary.srt 1.7 KB
  6. How to Code by Yourself (part 2).mp4 14.8 MB
  6. How to Code by Yourself (part 2).srt 14 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. How to Uncompress a .tar.gz file.mp4 5.4 MB
  7. How to Uncompress a .tar.gz file.srt 4.4 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. How to Succeed in this Course (Long Version).mp4 13 MB
  8. How to Succeed in this Course (Long Version).srt 15.5 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. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39 MB
  9. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 33.9 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 7.3 MB
  9. Updating the weights using gradient descent - Code.srt 2.5 KB
  Readme.txt 921.6 B
  [GigaCourse.com].url 0 B
  ▲ 119 total files

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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