Data Science: Deep Learning in Python
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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.
4.6 (1,606 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
13,241 students enrolled
Last updated 5/2017
English
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Includes:
  • 6 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Code a neural network from scratch in Python and numpy
  • Code a neural network using Google's TensorFlow
  • Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"
  • 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
  • Install TensorFlow
View Curriculum
Requirements
  • How to take partial derivatives and log-likelihoods (ex. finding the maximum likelihood estimations for a die)
  • Install Numpy and Python (approx. latest version of Numpy as of Jan 2016)
  • Don't worry about installing TensorFlow, we will do that in the lectures.
  • Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this 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 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.


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


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.


USEFUL COURSE ORDERING:

  • (The Numpy Stack in Python)
  • Linear Regression in Python
  • Logistic Regression in Python
  • (Supervised Machine Learning in Python)
  • (Bayesian Machine Learning in Python: A/B Testing)
  • Deep Learning in Python
  • Practical Deep Learning in Theano and TensorFlow
  • (Supervised Machine Learning in Python 2: Ensemble Methods)
  • Convolutional Neural Networks in Python
  • (Easy NLP)
  • (Cluster Analysis and Unsupervised Machine Learning)
  • Unsupervised Deep Learning
  • (Hidden Markov Models)
  • Recurrent Neural Networks in Python
  • Artificial Intelligence: Reinforcement Learning in Python
  • Natural Language Processing with Deep Learning in Python
Who is the target audience?
  • Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course
  • Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.
  • People who already know how to take partial derivatives and log-likelihoods. Since we cover this in more detail in my logistic regression class, it is not covered quite as thoroughly here.
  • People who already know how to code in Python and Numpy. You will need some familiarity because we go through it quite fast. Don't worry, it's not that hard.
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Curriculum For This Course
Expand All 55 Lectures Collapse All 55 Lectures 06:07:09
+
What is a neural network?
5 Lectures 27:27

Overview of the course and prerequisites.

Preview 03:45


Deep Learning Readiness Test
05:33

An almost purely qualitative description of neural networks.

Preview 04:20

Introduction to the E-Commerce Course Project
08:52
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Classifying more than 2 things at a time
13 Lectures 01:11:53
Prediction: Section Introduction and Outline
05:39

From Logistic Regression to Neural Networks
05:12

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

Softmax
02:54

Sigmoid vs. Softmax
01:30

Feedforward in Slow-Mo (part 1)
19:42

Feedforward in Slow-Mo (part 2)
10:55

Where to get the code for this course
01:30

How do we code the softmax in Python?

Softmax in Code
03:39

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

Building an entire feedforward neural network in Python
06:23

E-Commerce Course Project: Pre-Processing the Data
05:24

E-Commerce Course Project: Making Predictions
03:55

Prediction Quizzes
03:25

Prediction: Section Summary
01:45
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Training a neural network
12 Lectures 01:23:46
Training: Section Introduction and Outline
02:49

What do all these symbols and letters mean?
09:45

What does it mean to "train" a neural network?
06:15

Derivation of backpropagation from first principles. Defining the objective function, taking the log, and differentiating the log with respect to weights in each layer.

Backpropagation Intro
11:53

A further look into backpropagation.

Backpropagation - what does the weight update depend on?
04:47

Backpropagation for deeper networks, exposing the structure, and how to code it more efficiently.

Backpropagation - recursiveness
04:37

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

Backpropagation in code
17:07

The WRONG Way to Learn Backpropagation
03:52

E-Commerce Course Project: Training Logistic Regression with Softmax
08:11

E-Commerce Course Project: Training a Neural Network
06:19

Training Quiz
05:30

Training: Section Summary
02:41
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Practical Machine Learning
7 Lectures 23:04
Practical Issues: Section Introduction and Outline
01:43

What are the donut and XOR problems again?

Donut and XOR Review
01:06

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

Donut and XOR Revisited
04:21

sigmoid, tanh, relu along with their derivatives

Common nonlinearities and their derivatives
01:26

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

Hyperparameters and Cross-Validation
04:10

Manually Choosing Learning Rate and Regularization Penalty
04:08

Practical Issues: Section Summary
06:10
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TensorFlow, exercises, practice, and what to learn next
6 Lectures 41:35

A look at Google's new TensorFlow library.

TensorFlow plug-and-play example
07:31

Visualizing what a neural network has learned using TensorFlow Playground
11:35

What did you learn? What didn't you learn? Where can you learn more?
Where to go from here
03:41

You know more than you think you know
04:52

How to get good at deep learning + exercises
05:07

Deep neural networks in just 3 lines of code with Sci-Kit Learn
08:49
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Project: Facial Expression Recognition
6 Lectures 56:01
Facial Expression Recognition Problem Description
12:21

The class imbalance problem
06:01

Utilities walkthrough
05:45

Facial Expression Recognition in Code (Binary / Sigmoid)
12:13

Facial Expression Recognition in Code (Logistic Regression Softmax)
08:57

Facial Expression Recognition in Code (ANN Softmax)
10:44
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Appendix
6 Lectures 01:03:23
Gradient Descent Tutorial
04:30

Help with Softmax Derivative
04:09

Backpropagation with Softmax Troubleshooting
11:55

How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
17:32

How to Code by Yourself (part 1)
15:54

How to Code by Yourself (part 2)
09:23
About the Instructor
Lazy Programmer Inc.
4.6 Average rating
8,654 Reviews
47,898 Students
18 Courses
Data scientist and big data engineer

I am a data scientist, big data engineer, and full stack software engineer.

For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons communicate with their family and caregivers.

I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.