Data Science: Deep Learning in Python

A guide for writing your own neural network in Python and Numpy, and how to do it in Google's TensorFlow.

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Course Data

Lectures: 70
Length: 08h 25m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, 30-day money back guarantee

Course Description

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google's new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

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!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.

NOTE:

If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.

I have other courses that cover more advanced topics, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

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.

All the code for this course can be downloaded from my github:

https://github.com/lazyprogrammer/machine_learning_examples

In the directory: ann_class

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

  • calculus
  • linear algebra
  • probability
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations, loading a CSV file
  • linear regression, logistic regression


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.

Lectures

What is a neural network?

  1. Introduction and Outline (03:45) (FREE preview available)
  2. Neural Networks with No Math (04:20)
  3. Where does this course fit into your deep learning studies? (04:57)
  4. Deep Learning Readiness Test (05:33)
  5. Introduction to the E-Commerce Course Project (08:52)

Classifying more than 2 things at a time

  1. Prediction: Section Introduction and Outline (05:39)
  2. From Logistic Regression to Neural Networks (05:12)
  3. Interpreting the Weights of a Neural Network (08:05)
  4. Softmax (02:54)
  5. Sigmoid vs. Softmax (01:30)
  6. Feedforward in Slow-Mo (part 1) (19:42)
  7. Feedforward in Slow-Mo (part 2) (10:55)
  8. Where to get the code for this course (01:30)
  9. Softmax in Code (03:39)
  10. Building an entire feedforward neural network in Python (06:23)
  11. E-Commerce Course Project: Pre-Processing the Data (05:24)
  12. E-Commerce Course Project: Making Predictions (03:55)
  13. Prediction Quizzes (03:25)
  14. Prediction: Section Summary (01:45)

Training a neural network

  1. Training: Section Introduction and Outline (02:49)
  2. What do all these symbols and letters mean? (09:45)
  3. What does it mean to 'train' a neural network? (06:15)
  4. Backpropagation Intro (11:53)
  5. Backpropagation - what does the weight update depend on? (04:47)
  6. Backpropagation - recursiveness (04:37)
  7. Backpropagation in code (17:07)
  8. The WRONG Way to Learn Backpropagation (03:52)
  9. E-Commerce Course Project: Training Logistic Regression with Softmax (08:11)
  10. E-Commerce Course Project: Training a Neural Network (06:19)
  11. Training Quizzes (05:30)
  12. Training: Section Summary (02:41)

Practical Machine Learning

  1. Practical Issues: Section Introduction and Outline (01:43)
  2. Donut and XOR Review (01:06)
  3. Donut and XOR Revisited (04:21)
  4. Neural Networks for Regression (11:38)
  5. Common nonlinearities and their derivatives (01:26)
  6. Practical Considerations for Choosing Activation Functions (07:45)
  7. Hyperparameters and Cross-Validation (04:11)
  8. Manually Choosing Learning Rate and Regularization Penalty (04:08)
  9. Practical Issues: Section Summary (06:10)

TensorFlow, exercises, practice, and what to learn next

  1. TensorFlow plug-and-play example (07:31)
  2. Visualizing what a neural network has learned using TensorFlow Playground (11:35)
  3. Where to go from here (03:41)
  4. You know more than you think you know (04:52)
  5. How to get good at deep learning + exercises (05:07)
  6. Deep neural networks in just 3 lines of code with Sci-Kit Learn (08:49)

Project: Facial Expression Recognition

  1. Facial Expression Recognition Problem Description (12:21)
  2. The class imbalance problem (06:01)
  3. Utilities walkthrough (05:45)
  4. Facial Expression Recognition in Code (Binary / Sigmoid) (12:13)
  5. Facial Expression Recognition in Code (Logistic Regression Softmax) (08:57)
  6. Facial Expression Recognition in Code (ANN Softmax) (10:44)

Backpropagation Supplementary Lectures

  1. Backpropagation Supplementary Lectures Introduction (01:03)
  2. Why Learn the Ins and Outs of Backpropagation? (08:53)
  3. Gradient Descent Tutorial (04:30)
  4. Help with Softmax Derivative (04:09)
  5. Backpropagation with Softmax Troubleshooting (11:55)

Appendix

  1. What is the Appendix? (02:48)
  2. What's the difference between "neural networks" and "deep learning"? (07:58)
  3. Windows-Focused Environment Setup 2018 (20:20)
  4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:22)
  5. How to Code Yourself (part 1) (15:54)
  6. How to Code Yourself (part 2) (09:23)
  7. How to Succeed in this Course (Long Version) (10:24)
  8. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:04)
  9. How to Uncompress a .tar.gz file (03:18)
  10. Python 2 vs Python 3 (04:38)
  11. What order should I take your courses in? (part 1) (11:18)
  12. What order should I take your courses in? (part 2) (16:07)
  13. Where to get discount coupons and FREE deep learning material (02:20)