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

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

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Description
Udemy - Deep Learning Prerequisites Logistic Regression in Python







Description



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!



If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want use your skills to make data-driven decisions and optimize your business using scientific principles, then this course is for you.



This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.



Suggested Prerequisites:



calculus (taking derivatives)

matrix arithmetic

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file



TIPS (for getting through the course):



Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.



WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:



Deep Learning Prerequisites: Logistic Regression in Python Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

Adult learners who want to get into the field of data science and big data

Students who are thinking of pursuing machine learning or data science

Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python

People who know some machine learning but want to be able to relate it to artificial intelligence

People who are interested in bridging the gap between computational neuroscience and machine learning



Created by Lazy Programmer Inc.

Last updated 3/2020

English

English [Auto-generated]

File list
  • [GigaCourse.com] Udemy - Deep Learning Prerequisites Logistic Regression in Python
  • 1. Start Here/1. Introduction and Outline.mp4 46.9 MB
  • 1. Start Here/1. Introduction and Outline.srt 5.3 KB
  • 1. Start Here/2. How to Succeed in this Course.mp4 6.4 MB
  • 1. Start Here/2. How to Succeed in this Course.srt 4 KB
  • 1. Start Here/3. Review of the classification problem.mp4 3 MB
  • 1. Start Here/3. Review of the classification problem.srt 2.2 KB
  • 1. Start Here/4. Introduction to the E-Commerce Course Project.mp4 14.8 MB
  • 1. Start Here/4. Introduction to the E-Commerce Course Project.srt 7.6 MB
  • 1. Start Here/5. Easy first quiz.html 152 B
  • 2. Basics What is linear classification What's the relation to neural networks/1. Linear Classification.mp4 7.6 MB
  • 2. Basics What is linear classification What's the relation to neural networks/1. Linear Classification.srt 5.2 KB
  • 2. Basics What is linear classification What's the relation to neural networks/2. Biological inspiration - the neuron.mp4 9.4 MB
  • 2. Basics What is linear classification What's the relation to neural networks/2. Biological inspiration - the neuron.srt 4.4 KB
  • 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 15.2 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 80.2 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 5.8 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.srt 4.5 KB
  • 2. Basics What is linear classification What's the relation to neural networks/5. Interpretation of Logistic Regression Output.mp4 27.9 MB
  • 2. Basics What is linear classification What's the relation to neural networks/5. Interpretation of Logistic Regression Output.srt 6.4 KB
  • 2. Basics What is linear classification What's the relation to neural networks/6. E-Commerce Course Project Pre-Processing the Data.mp4 11.2 MB
  • 2. Basics What is linear classification What's the relation to neural networks/6. E-Commerce Course Project Pre-Processing the Data.srt 5.1 KB
  • 2. Basics What is linear classification What's the relation to neural networks/7. E-Commerce Course Project Making Predictions.mp4 5.7 MB
  • 2. Basics What is linear classification What's the relation to neural networks/7. E-Commerce Course Project Making Predictions.srt 3 KB
  • 2. Basics What is linear classification What's the relation to neural networks/8. Feedforward Quiz.mp4 2.3 MB
  • 2. Basics What is linear classification What's the relation to neural networks/8. Feedforward Quiz.srt 1.7 KB
  • 2. Basics What is linear classification What's the relation to neural networks/9. Prediction Section Summary.mp4 2.2 MB
  • 2. Basics What is linear classification What's the relation to neural networks/9. Prediction Section Summary.srt 1.5 KB
  • 3. Solving for the optimal weights/1. Training Section Introduction.mp4 2.8 MB
  • 3. Solving for the optimal weights/1. Training Section Introduction.srt 2 KB
  • 3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.mp4 17.1 MB
  • 3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.srt 5.3 KB
  • 3. Solving for the optimal weights/11. Training Section Summary.mp4 3.4 MB
  • 3. Solving for the optimal weights/11. Training Section Summary.srt 2.6 KB
  • 3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.mp4 9.1 MB
  • 3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.srt 7.3 KB
  • 3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..mp4 6.4 MB
  • 3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..srt 5.2 KB
  • 3. Solving for the optimal weights/4. The cross-entropy error function - Theory.mp4 4.5 MB
  • 3. Solving for the optimal weights/4. The cross-entropy error function - Theory.srt 4.4 KB
  • 3. Solving for the optimal weights/5. The cross-entropy error function - Code.mp4 9.1 MB
  • 3. Solving for the optimal weights/5. The cross-entropy error function - Code.srt 3.9 KB
  • 3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.mp4 5.3 MB
  • 3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.srt 2.3 KB
  • 3. Solving for the optimal weights/7. Maximizing the likelihood.mp4 25.2 MB
  • 3. Solving for the optimal weights/7. Maximizing the likelihood.srt 4 KB
  • 3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.mp4 9.3 MB
  • 3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.srt 8.1 KB
  • 3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.mp4 7.3 MB
  • 3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.srt 2.5 KB
  • 4. Practical concerns/1. Practical Section Introduction.mp4 4.7 MB
  • 4. Practical concerns/1. Practical Section Introduction.srt 3.5 KB
  • 4. Practical concerns/10. Why Divide by Square Root of D.mp4 23.5 MB
  • 4. Practical concerns/10. Why Divide by Square Root of D.srt 8.7 KB
  • 4. Practical concerns/11. Practical Section Summary.mp4 3.4 MB
  • 4. Practical concerns/11. Practical Section Summary.srt 78.3 MB
  • 4. Practical concerns/2. Interpreting the Weights.mp4 6.3 MB
  • 4. Practical concerns/2. Interpreting the Weights.srt 4.7 KB
  • 4. Practical concerns/3. L2 Regularization - Theory.mp4 14.7 MB
  • 4. Practical concerns/3. L2 Regularization - Theory.srt 11.5 KB
  • 4. Practical concerns/4. L2 Regularization - Code.mp4 4.5 MB
  • 4. Practical concerns/4. L2 Regularization - Code.srt 1.6 KB
  • 4. Practical concerns/5. L1 Regularization - Theory.mp4 4.4 MB
  • 4. Practical concerns/5. L1 Regularization - Theory.srt 14.9 MB
  • 4. Practical concerns/6. L1 Regularization - Code.mp4 12 MB
  • 4. Practical concerns/6. L1 Regularization - Code.srt 4.6 KB
  • 4. Practical concerns/7. L1 vs L2 Regularization.mp4 4.8 MB
  • 4. Practical concerns/7. L1 vs L2 Regularization.srt 4.3 KB
  • 4. Practical concerns/8. The donut problem.mp4 24.7 MB
  • 4. Practical concerns/8. The donut problem.srt 7.4 KB
  • 4. Practical concerns/9. The XOR problem.mp4 14.2 MB
  • 4. Practical concerns/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
  • 5. Checkpoint and applications How to make sure you know your stuff/1. BONUS Sentiment Analysis.srt 6.4 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.mp4 4 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.srt 3.4 KB
  • 5. Checkpoint and applications How to make sure you know your stuff/3. BONUS Exercises + how to get good at this.mp4 5.3 MB
  • 5. Checkpoint and applications How to make sure you know your stuff/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
  • 6. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.srt 6.5 KB
  • 6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp4 21.4 MB
  • 6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.srt 16 KB
  • 6. Project Facial Expression Recognition/3. The class imbalance problem.mp4 10.1 MB
  • 6. Project Facial Expression Recognition/3. The class imbalance problem.srt 8 KB
  • 6. Project Facial Expression Recognition/4. Utilities walkthrough.mp4 13.5 MB
  • 6. Project Facial Expression Recognition/4. Utilities walkthrough.srt 5.8 KB
  • 6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp4 24 MB
  • 6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.srt 8.1 KB
  • 6. Project Facial Expression Recognition/6. Facial Expression Recognition Project Summary.mp4 2.9 MB
  • 6. Project Facial Expression Recognition/6. Facial Expression Recognition Project Summary.srt 1.7 KB
  • 7. Appendix FAQ/1. What is the Appendix.mp4 5.5 MB
  • 7. Appendix FAQ/1. What is the Appendix.srt 3.8 KB
  • 7. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
  • 7. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.srt 78.3 MB
  • 7. Appendix FAQ/11. Python 2 vs Python 3.mp4 7.8 MB
  • 7. Appendix FAQ/11. Python 2 vs Python 3.srt 6.6 KB
  • 7. Appendix FAQ/12. What order should I take your courses in (part 1).mp4 29.3 MB
  • 7. Appendix FAQ/12. What order should I take your courses in (part 1).srt 17.1 KB
  • 7. Appendix FAQ/13. What order should I take your courses in (part 2).mp4 37.6 MB
  • 7. Appendix FAQ/13. What order should I take your courses in (part 2).srt 25.1 KB

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