The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow
What you'll learn
·
Learn how Deep Learning REALLY works (not just some diagrams and
magical black box code)
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Learn how a neural network is built from basic building blocks
(the neuron)
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Code a neural network from scratch in Python and numpy
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Code a neural network using Google's TensorFlow
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Describe different types of neural networks and the different
types of problems they are used for
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Derive the backpropagation rule from first principles
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Create a neural network with an output that has K > 2 classes
using softmax
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Describe the various terms related to neural networks, such as
"activation", "backpropagation" and "feedforward"
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Install TensorFlow
Requirements
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Basic math (calculus derivatives, matrix arithmetic,
probability)
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Install Numpy and Python
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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 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.
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