Data Science: Deep Learning and Neural Networks in Python
What you’ll learn

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 fullon nonlinear 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 GPUoptimization, check out my followup 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 (taking derivatives)
 matrix arithmetic
 probability
 Python coding: if/else, loops, lists, dicts, sets
 Numpy coding: matrix and vector operations, loading a CSV file
 Be familiar with basic linear models such as linear regression and logistic regression
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
 Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)
Who this course is for:
 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.
Jim Hargreaves –
This course teaches deep learning fundamentals well, but most importantly it teaches you how to THINK like a data scientist!
Definitely do it if you want to learn how deep learning works at its core.
Stephanie Turcotte –
The beginning is a bit scary thanks to the demanding review section, but as you get to the rest of the course it all comes together and you understand why you needed to do it. This is the only course that goes through backpropagation in so much detail, I loved it.
Ravi Zutshi –
Course was great. It’s made for people with a little background in math and some Python programming skills. You helped me become a ML engineer from beginner to advanced. Thanks
Vanessa Markaki –
The structure of the course was a little difficult to follow. I would have preferred the code to be deployed directly after the theoretical formulation of each problem. And I would still like a couple of examples from the git files to be formulated as an exercise. But I don’t know if they exist in your other courses. Otherwise it was very interesting.
Austin Hong –
The introduction suggests to me that this is exactly what I’m looking for to get into deep learning! This is a unique, yet familiar experience coming from a science background. I find myself being a student again and I’m delighted about the lessons I’ve learned so far!
Benjamin –
Great course! I finally understand backpropagation.
Sanjay Sundaram –
So far am happy with how the structure of the course is taking place. Good course worth your time and money.
Sanjay Sarangi –
An intermediateadvanced level course for deep learning which explains the algorithms in the simplest and easy to understand way. Bravo.
Ravi Rajput –
Wonderful content and well organized course. Very much recommended to the beginners and advanced to start with deep learning basics with enough handson practice for real time application.
Shadi Parsa –
It was great. Some things a little more difficult because of rusty math skill but that is okay!
Amirul Haque –
I am feeling extremely grateful to have this course. The course covers fundamental concepts and building blocks of deep learning and neural networks.
Jeet Ghotra –
I have really enjoyed the course. It’s very well structured and the important thing is it makes me practice more my math and coding.