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

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

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收录时间：2021-04-16
最近下载：2024-12-10

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文件列表

  • 7. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 195.3 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.srt 84.1 MB
  • 7. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.srt 82.1 MB
  • 7. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
  • 4. Practical concerns/11. Practical Section Summary.srt 82.1 MB
  • 1. Start Here/1. Introduction and Outline.mp4 49.2 MB
  • 7. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
  • 7. Appendix FAQ/9. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
  • 7. Appendix FAQ/14. BONUS Where to get discount coupons and FREE deep learning material.mp4 39.7 MB
  • 7. Appendix FAQ/13. What order should I take your courses in (part 2).mp4 39.4 MB
  • 7. Appendix FAQ/12. What order should I take your courses in (part 1).mp4 30.7 MB
  • 2. Basics What is linear classification What's the relation to neural networks/5. Interpretation of Logistic Regression Output.mp4 29.2 MB
  • 3. Solving for the optimal weights/7. Maximizing the likelihood.mp4 26.4 MB
  • 4. Practical concerns/8. The donut problem.mp4 25.9 MB
  • 7. Appendix FAQ/5. How to Code by Yourself (part 1).mp4 25.7 MB
  • 6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp4 25.2 MB
  • 4. Practical concerns/10. Why Divide by Square Root of D.mp4 24.6 MB
  • 7. Appendix FAQ/2. Gradient Descent Tutorial.mp4 23.9 MB
  • 6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp4 22.5 MB
  • 3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.mp4 17.9 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.mp4 16.0 MB
  • 4. Practical concerns/5. L1 Regularization - Theory.srt 15.7 MB
  • 7. Appendix FAQ/6. How to Code by Yourself (part 2).mp4 15.5 MB
  • 1. Start Here/4. Introduction to the E-Commerce Course Project.mp4 15.5 MB
  • 4. Practical concerns/3. L2 Regularization - Theory.mp4 15.4 MB
  • 4. Practical concerns/9. The XOR problem.mp4 14.9 MB
  • 6. Project Facial Expression Recognition/4. Utilities walkthrough.mp4 14.1 MB
  • 7. Appendix FAQ/8. How to Succeed in this Course (Long Version).mp4 13.6 MB
  • 4. Practical concerns/6. L1 Regularization - Code.mp4 12.6 MB
  • 5. Checkpoint and applications How to make sure you know your stuff/1. BONUS Sentiment Analysis.mp4 12.0 MB
  • 2. Basics What is linear classification What's the relation to neural networks/6. E-Commerce Course Project Pre-Processing the Data.mp4 11.7 MB
  • 6. Project Facial Expression Recognition/3. The class imbalance problem.mp4 10.6 MB
  • 6. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.mp4 10.3 MB
  • 2. Basics What is linear classification What's the relation to neural networks/2. Biological inspiration - the neuron.mp4 9.8 MB
  • 3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.mp4 9.8 MB
  • 3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.mp4 9.5 MB
  • 3. Solving for the optimal weights/5. The cross-entropy error function - Code.mp4 9.5 MB
  • 7. Appendix FAQ/11. Python 2 vs Python 3.mp4 8.2 MB
  • 1. Start Here/4. Introduction to the E-Commerce Course Project.srt 8.0 MB
  • 2. Basics What is linear classification What's the relation to neural networks/1. Linear Classification.mp4 7.9 MB
  • 3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.mp4 7.6 MB
  • 1. Start Here/2. How to Succeed in this Course.mp4 6.7 MB
  • 3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..mp4 6.7 MB
  • 4. Practical concerns/2. Interpreting the Weights.mp4 6.6 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.mp4 6.1 MB
  • 2. Basics What is linear classification What's the relation to neural networks/7. E-Commerce Course Project Making Predictions.mp4 6.0 MB
  • 7. Appendix FAQ/1. What is the Appendix.mp4 5.7 MB
  • 7. Appendix FAQ/7. How to Uncompress a .tar.gz file.mp4 5.7 MB
  • 3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.mp4 5.5 MB
  • 5. Checkpoint and applications How to make sure you know your stuff/3. BONUS Exercises + how to get good at this.mp4 5.5 MB
  • 4. Practical concerns/7. L1 vs L2 Regularization.mp4 5.0 MB
  • 4. Practical concerns/1. Practical Section Introduction.mp4 5.0 MB
  • 3. Solving for the optimal weights/4. The cross-entropy error function - Theory.mp4 4.7 MB
  • 4. Practical concerns/4. L2 Regularization - Code.mp4 4.7 MB
  • 4. Practical concerns/5. L1 Regularization - Theory.mp4 4.6 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.mp4 4.2 MB
  • 4. Practical concerns/11. Practical Section Summary.mp4 3.6 MB
  • 3. Solving for the optimal weights/11. Training Section Summary.mp4 3.6 MB
  • 1. Start Here/3. Review of the classification problem.mp4 3.1 MB
  • 6. Project Facial Expression Recognition/6. Facial Expression Recognition Project Summary.mp4 3.1 MB
  • 3. Solving for the optimal weights/1. Training Section Introduction.mp4 3.0 MB
  • 2. Basics What is linear classification What's the relation to neural networks/8. Feedforward Quiz.mp4 2.4 MB
  • 2. Basics What is linear classification What's the relation to neural networks/9. Prediction Section Summary.mp4 2.3 MB
  • 7. Appendix FAQ/9. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 34.7 kB
  • 7. Appendix FAQ/13. What order should I take your courses in (part 2).srt 25.7 kB
  • 7. Appendix FAQ/5. How to Code by Yourself (part 1).srt 24.9 kB
  • 7. Appendix FAQ/3. Windows-Focused Environment Setup 2018.srt 22.2 kB
  • 7. Appendix FAQ/12. What order should I take your courses in (part 1).srt 17.5 kB
  • 6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.srt 16.4 kB
  • 7. Appendix FAQ/8. How to Succeed in this Course (Long Version).srt 15.9 kB
  • 7. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 15.8 kB
  • 7. Appendix FAQ/6. How to Code by Yourself (part 2).srt 14.4 kB
  • 4. Practical concerns/3. L2 Regularization - Theory.srt 11.8 kB
  • 4. Practical concerns/10. Why Divide by Square Root of D.srt 8.9 kB
  • 7. Appendix FAQ/14. BONUS Where to get discount coupons and FREE deep learning material.srt 8.6 kB
  • 3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.srt 8.3 kB
  • 6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.srt 8.3 kB
  • 6. Project Facial Expression Recognition/3. The class imbalance problem.srt 8.1 kB
  • 4. Practical concerns/8. The donut problem.srt 7.5 kB
  • 3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.srt 7.5 kB
  • 7. Appendix FAQ/11. Python 2 vs Python 3.srt 6.8 kB
  • 6. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.srt 6.6 kB
  • 5. Checkpoint and applications How to make sure you know your stuff/1. BONUS Sentiment Analysis.srt 6.6 kB
  • 2. Basics What is linear classification What's the relation to neural networks/5. Interpretation of Logistic Regression Output.srt 6.5 kB
  • 4. Practical concerns/9. The XOR problem.srt 6.2 kB
  • 7. Appendix FAQ/2. Gradient Descent Tutorial.srt 6.1 kB
  • 6. Project Facial Expression Recognition/4. Utilities walkthrough.srt 6.0 kB
  • 1. Start Here/1. Introduction and Outline.srt 5.4 kB
  • 3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.srt 5.4 kB
  • 3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..srt 5.3 kB
  • 2. Basics What is linear classification What's the relation to neural networks/1. Linear Classification.srt 5.3 kB
  • 2. Basics What is linear classification What's the relation to neural networks/6. E-Commerce Course Project Pre-Processing the Data.srt 5.3 kB
  • 4. Practical concerns/2. Interpreting the Weights.srt 4.8 kB
  • 4. Practical concerns/6. L1 Regularization - Code.srt 4.7 kB
  • 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.srt 4.6 kB
  • 3. Solving for the optimal weights/4. The cross-entropy error function - Theory.srt 4.5 kB
  • 7. Appendix FAQ/7. How to Uncompress a .tar.gz file.srt 4.5 kB
  • 2. Basics What is linear classification What's the relation to neural networks/2. Biological inspiration - the neuron.srt 4.5 kB
  • 4. Practical concerns/7. L1 vs L2 Regularization.srt 4.4 kB
  • 1. Start Here/2. How to Succeed in this Course.srt 4.1 kB
  • 3. Solving for the optimal weights/7. Maximizing the likelihood.srt 4.1 kB
  • 3. Solving for the optimal weights/5. The cross-entropy error function - Code.srt 4.0 kB
  • 5. Checkpoint and applications How to make sure you know your stuff/3. BONUS Exercises + how to get good at this.srt 3.9 kB
  • 7. Appendix FAQ/1. What is the Appendix.srt 3.9 kB
  • 4. Practical concerns/1. Practical Section Introduction.srt 3.6 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.srt 3.5 kB
  • 2. Basics What is linear classification What's the relation to neural networks/7. E-Commerce Course Project Making Predictions.srt 3.1 kB
  • 3. Solving for the optimal weights/11. Training Section Summary.srt 2.6 kB
  • 3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.srt 2.5 kB
  • 3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.srt 2.3 kB
  • 1. Start Here/3. Review of the classification problem.srt 2.3 kB
  • 3. Solving for the optimal weights/1. Training Section Introduction.srt 2.1 kB
  • 6. Project Facial Expression Recognition/6. Facial Expression Recognition Project Summary.srt 1.8 kB
  • 2. Basics What is linear classification What's the relation to neural networks/8. Feedforward Quiz.srt 1.7 kB
  • 4. Practical concerns/4. L2 Regularization - Code.srt 1.7 kB
  • 2. Basics What is linear classification What's the relation to neural networks/9. Prediction Section Summary.srt 1.5 kB
  • Readme.txt 962 Bytes
  • 1. Start Here/5. Easy first quiz.html 152 Bytes
  • [GigaCourse.com].url 49 Bytes

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