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Data Science: Deep Learning in Python

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow
Instructor:
Lazy Programmer Inc.
34,664 students enrolled
English [Auto-generated] More
Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
Learn how a neural network is built from basic building blocks (the neuron)
Code a neural network from scratch in Python and numpy
Code a neural network using Google's TensorFlow
Describe different types of neural networks and the different types of problems they are used for
Derive the backpropagation rule from first principles
Create a neural network with an output that has K > 2 classes using softmax
Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"
Install TensorFlow

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 NetworksRestricted Boltzmann MachinesAutoencoders, 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.

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

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?:

  • 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)

Welcome

1
Introduction and Outline

Overview of the course and prerequisites.

2
Where does this course fit into your deep learning studies?
3
Where to get the code
4
How to Succeed in this Course

Review

1
Review Section Introduction
2
What does machine learning do?
3
Neuron Predictions
4
Neuron Training
5
Deep Learning Readiness Test
6
Review Section Summary

Preliminaries: From Neurons to Neural Networks

1
Neural Networks with No Math

An almost purely qualitative description of neural networks.

2
Introduction to the E-Commerce Course Project

Classifying more than 2 things at a time

1
Prediction: Section Introduction and Outline
2
From Logistic Regression to Neural Networks
3
Interpreting the Weights of a Neural Network
4
Softmax

What's the function we use to classify more than 2 things?

5
Sigmoid vs. Softmax
6
Feedforward in Slow-Mo (part 1)
7
Feedforward in Slow-Mo (part 2)
8
Where to get the code for this course
9
Softmax in Code

How do we code the softmax in Python?

10
Building an entire feedforward neural network in Python

Let's extend softmax and code the entire calculation from input to output.

11
E-Commerce Course Project: Pre-Processing the Data
12
E-Commerce Course Project: Making Predictions
13
Prediction Quizzes
14
Prediction: Section Summary

Training a neural network

1
Training: Section Introduction and Outline
2
What do all these symbols and letters mean?
3
What does it mean to "train" a neural network?
4
How to Brace Yourself to Learn Backpropagation
5
Categorical Cross-Entropy Loss Function
6
Training Logistic Regression with Softmax (part 1)
7
Training Logistic Regression with Softmax (part 2)
8
Backpropagation (part 1)
9
Backpropagation (part 2)
10
Backpropagation in code

How to code bacpropagation in Python using numpy operations vs. slow for loops.

11
Backpropagation (part 3)
12
The WRONG Way to Learn Backpropagation
13
E-Commerce Course Project: Training Logistic Regression with Softmax
14
E-Commerce Course Project: Training a Neural Network
15
Training Quiz
16
Training: Section Summary

Practical Machine Learning

1
Practical Issues: Section Introduction and Outline
2
Donut and XOR Review

What are the donut and XOR problems again?

3
Donut and XOR Revisited

We look again at the XOR and donut problem from logistic regression. The features are now learned automatically.

4
Neural Networks for Regression
5
Common nonlinearities and their derivatives

sigmoid, tanh, relu along with their derivatives

6
Practical Considerations for Choosing Activation Functions
7
Hyperparameters and Cross-Validation

Tips on choosing learning rate, regularization penalty, number of hidden units, and number of hidden layers.

8
Manually Choosing Learning Rate and Regularization Penalty
9
Practical Issues: Section Summary

TensorFlow, exercises, practice, and what to learn next

1
TensorFlow plug-and-play example

A look at Google's new TensorFlow library.

2
Visualizing what a neural network has learned using TensorFlow Playground
3
Where to go from here
What did you learn? What didn't you learn? Where can you learn more?
4
You know more than you think you know
5
How to get good at deep learning + exercises
6
Deep neural networks in just 3 lines of code with Sci-Kit Learn

Project: Facial Expression Recognition

1
Facial Expression Recognition Project Introduction
2
Facial Expression Recognition Problem Description
3
The class imbalance problem
4
Utilities walkthrough
5
Facial Expression Recognition in Code (Binary / Sigmoid)
6
Facial Expression Recognition in Code (Logistic Regression Softmax)
7
Facial Expression Recognition in Code (ANN Softmax)
8
Facial Expression Recognition Project Summary

Backpropagation Supplementary Lectures

1
Backpropagation Supplementary Lectures Introduction
2
Why Learn the Ins and Outs of Backpropagation?
3
Gradient Descent Tutorial
4
Help with Softmax Derivative
5
Backpropagation with Softmax Troubleshooting

Appendix

1
What is the Appendix?
2
What's the difference between "neural networks" and "deep learning"?
3
Windows-Focused Environment Setup 2018
4
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
5
How to Code by Yourself (part 1)
6
How to Code by Yourself (part 2)
7
How to Succeed in this Course (Long Version)
8
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
9
Proof that using Jupyter Notebook is the same as not using it
10
How to Uncompress a .tar.gz file
11
BONUS: Where to get Udemy coupons and FREE deep learning material
12
Python 2 vs Python 3
13
Where does this course fit into your deep learning studies? (Old Version)
14
What order should I take your courses in? (part 1)
15
What order should I take your courses in? (part 2)
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