Data Science: Deep Learning and Neural Networks 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: 93
Length: 11h 49m
Skill Level: All Levels
Languages: English
Includes: Lifetime access

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.


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.

"If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

  • calculus
  • matrix arithmetic (adding, multiplying)
  • 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 success:

  • Use the video speed changer! Personally, I like to watch 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!
  • Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
  • Write code yourself, this is an applied course! Don't be a "couch potato".



3 Lectures · 21min
  1. Introduction and Outline (06:33) (FREE preview available)
  2. Where to get the code (09:21)
  3. How to Succeed in this Course (05:52)


6 Lectures · 30min
  1. Review Section Introduction (01:59)
  2. What does machine learning do? (05:28)
  3. Neuron Predictions (05:01)
  4. Neuron Training (08:47)
  5. Deep Learning Readiness Test (05:33)
  6. Review Section Summary (03:52)

Preliminaries: From Neurons to Neural Networks

2 Lectures · 13min
  1. Neural Networks with No Math (04:20)
  2. Introduction to the E-Commerce Course Project (08:52)

Classifying more than 2 things at a time

15 Lectures · 01hr 23min
  1. Prediction: Section Introduction and Outline (05:40)
  2. From Logistic Regression to Neural Networks (05:13)
  3. Interpreting the Weights of a Neural Network (08:05)
  4. Softmax (02:55)
  5. Sigmoid vs. Softmax (01:31)
  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 (03:26)
  14. Prediction: Section Summary (01:45)
  15. Suggestion Box (03:03)

Training a neural network

16 Lectures · 02hr 14min
  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? (06:46)
  4. How to Brace Yourself to Learn Backpropagation (07:39)
  5. Categorical Cross-Entropy Loss Function (11:01)
  6. Training Logistic Regression with Softmax (part 1) (14:42)
  7. Training Logistic Regression with Softmax (part 2) (05:41)
  8. Backpropagation (part 1) (05:14)
  9. Backpropagation (part 2) (10:50)
  10. Backpropagation in code (17:07)
  11. Backpropagation (part 3) (16:12)
  12. The WRONG Way to Learn Backpropagation (03:52)
  13. E-Commerce Course Project: Training Logistic Regression with Softmax (08:11)
  14. E-Commerce Course Project: Training a Neural Network (06:19)
  15. Training Quizzes (05:30)
  16. Training: Section Summary (02:41)

Practical Machine Learning

9 Lectures · 42min
  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 (11:39)
  5. Common nonlinearities and their derivatives (01:26)
  6. Practical Considerations for Choosing Activation Functions (07:46)
  7. Hyperparameters and Cross-Validation (04:12)
  8. Manually Choosing Learning Rate and Regularization Penalty (04:09)
  9. Practical Issues: Section Summary (06:10)

TensorFlow, exercises, practice, and what to learn next

6 Lectures · 53min
  1. TensorFlow plug-and-play example (19:18)
  2. Visualizing what a neural network has learned using TensorFlow Playground (11:35)
  3. Where to go from here (03:42)
  4. You know more than you think you know (04:53)
  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 (08:50)

Project: Facial Expression Recognition

6 Lectures · 56min
  1. Facial Expression Recognition Problem Description (12:21)
  2. The class imbalance problem (06:02)
  3. Utilities walkthrough (05:46)
  4. Facial Expression Recognition in Code (Binary / Sigmoid) (12:14)
  5. Facial Expression Recognition in Code (Logistic Regression Softmax) (08:57)
  6. Facial Expression Recognition in Code (ANN Softmax) (10:45)

Backpropagation Legacy Lectures

4 Lectures · 27min
  1. What does it mean to 'train' a neural network? (06:15)
  2. Backpropagation Intro (11:53)
  3. Backpropagation - what does the weight update depend on? (04:47)
  4. Backpropagation - recursiveness (04:37)

Backpropagation Supplementary Lectures

5 Lectures · 30min
  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)

Higher-Level Discussion

4 Lectures · 38min
  1. What's the difference between "neural networks" and "deep learning"? (07:59)
  2. Who should take this course in 2020 and beyond? (08:48)
  3. Who should learn backpropagation in 2020 and beyond? (11:19)
  4. Where does this course fit into your deep learning studies? (10:44)

More VIP Content

1 Lectures · 10min
  1. Data Science Interview Questions: Numerically Stable Cross-Entropy (10:33)

Welcome (Legacy)

2 Lectures · 06min
  1. Promo (Legacy) (03:08)
  2. Introduction and Outline (03:45)

Misc (Legacy)

1 Lectures · 07min
  1. TensorFlow plug-and-play example (07:32)

Extra Help

1 Lectures · 03min
  1. How to Uncompress a .tar.gz file (03:18)

Setting Up Your Environment (Appendix/FAQ by Student Request)

2 Lectures · 37min
  1. Windows-Focused Environment Setup 2018 (20:21)
  2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

4 Lectures · 42min
  1. How to Code Yourself (part 1) (15:55)
  2. How to Code Yourself (part 2) (09:24)
  3. Proof that using Jupyter Notebook is the same as not using it (12:29)
  4. Python 2 vs Python 3 (04:38)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:31)


  • Calculus Cheatsheet
  • Code for Neural Network with Arbitrary Number of Layers
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