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Deep Learning BLACK FRIDAY SALE – Everything $10

November 16, 2017

A lot of you have been asking me… “When is the $10 sale coming back?”

And as you know, I share the news as soon as I find out – so here it is.

Black Friday is THE BIGGEST SALE OF THE YEAR.

This is the lowest price possible on Udemy.

I always make sure to mention to everyone: grab everything while you can because we just don’t know when the next big sale is going to be!

Don’t get stuck for months wondering… “When is the next $10 sale coming back?” Just get everything now! (Even if you don’t plan on taking the course for some time.)

Enough babbling, let’s get to the coupons. Remember: there’s no need to type in the coupon code manually – I’ve already provided the links so all you need to do is click and add to cart!

But just in case you’re curious – the coupon code is BLACKFRIDAY2017.

Also, make sure you scroll down to the bottom for some important updates.

linear

https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=BLACKFRIDAY2017

log

https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=BLACKFRIDAY2017

 

deep1

https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=BLACKFRIDAY2017

 

nlp

https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=BLACKFRIDAY2017

 

deep2

https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=BLACKFRIDAY2017

 

sql

https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=BLACKFRIDAY2017

 

cnn

https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=BLACKFRIDAY2017

 

cluster

https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=BLACKFRIDAY2017

 

udeep

https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=BLACKFRIDAY2017

 

hmm

https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=BLACKFRIDAY2017

 

rnn

https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=BLACKFRIDAY2017

 

deepnlp

https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=BLACKFRIDAY2017

 

super

https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=BLACKFRIDAY2017

 

bayes

https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=BLACKFRIDAY2017

 

ensemble

https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=BLACKFRIDAY2017

 

rl

https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=BLACKFRIDAY2017

 

deeprl

https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=BLACKFRIDAY2017

 

gan

https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/?couponCode=BLACKFRIDAY2017

 

 

 

MODERN DEEP LEARNING IN PYTHON VIP VERSION

In my last post, I relaunched my course “Modern Deep Learning in Python” which has more than doubled in size since its inception. Along with this re-release I offered a special VIP version of the course where you get a free 28-page tutorial on Tensorflow’s new Estimator API.

As mentioned, this deal is not going to last. In fact, 24 hours from now, it will be GONE FOREVER. Remember: you MUST use the VIP coupon to get the VIP material.

 

 

PREREQUISITE COURSE COUPONS

And just as important, $10 coupons for some helpful prerequisite courses. You NEED to know this stuff before you study machine learning:

General (site-wide): http://bit.ly/2oCY14Z
Python http://bit.ly/2pbXxXz
Calc 1 http://bit.ly/2okPUib
Calc 2 http://bit.ly/2oXnhpX
Calc 3 http://bit.ly/2pVU0gQ
Linalg 1 http://bit.ly/2oBBir1
Linalg 2 http://bit.ly/2q5SGEE
Probability (option 1) http://bit.ly/2prFQ7o
Probability (option 2) http://bit.ly/2p8kcC0
Probability (option 3) http://bit.ly/2oXa2pb
Probability (option 4) http://bit.ly/2oXbZSK

 
OTHER UDEMY COURSE COUPONS

As you know, I’m the “Lazy Programmer”, not just the “Lazy Data Scientist” – I love all kinds of programming!

And I’ve got sales for everything:

iOS courses:
https://lazyprogrammer.me/ios

Android courses:
https://lazyprogrammer.me/android

Ruby on Rails courses:
https://lazyprogrammer.me/ruby-on-rails

Python courses:
https://lazyprogrammer.me/python

Big Data (Spark + Hadoop) courses:
https://lazyprogrammer.me/big-data-hadoop-spark-sql

Javascript, ReactJS, AngularJS courses:
https://lazyprogrammer.me/js

 

EVEN MORE COOL STUFF

Into Yoga in your spare time? Photography? Painting? There are courses, and I’ve got coupons! If you find a course on Udemy that you’d like a coupon for, just let me know and I’ll hook you up!
Remember, these links will self-destruct on November 28 (13 days). Act NOW!

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Modern Deep Learning in Python – RELAUNCH and VIP version!

November 12, 2017

Screen Shot 2017-11-12 at 2.52.31 PM

If you’ve been following my updates, you may have noticed that I’ve been hard at work doubling the size of my course, “Deep Learning part 2”, otherwise known as “Practical Deep Learning in Theano and Tensorflow”.

I’ve since renamed the course to “Modern Deep Learning in Python” and I am officially re-launching it today!

At this point, I have completed all the major updates I’ve had in my pipeline: extending the modern regularization section, adding batch normalization, Adam, and more code for other modern libraries like Keras, PyTorch, CNTK, and MXNet.

As part of this relaunch, I am releasing a VIP version of this course.

What do you get?

Well, in addition to the HOURS of free content I’ve just added to the course, you may have heard of Tensorflow’s new Estimator API.

It was released just a few months ago.

Why is it better?

  • Greatly simplifies machine learning programming
  • No need to deal with Graphs or Sessions
  • Encapsulates training, evaluation, prediction, and exporting models
  • Provides standard models so you don’t have to write any of the code yourself

You may have noticed that writing Tensorflow code can be quite repetitive. We need to define each layer, combine the layers to calculate the output, create a loss function, create an optimizer, initialize the variables, run the optimizer, plot the loss, and so on.

All “boilerplate” stuff (although helpful to repeat if you are in the process of learning Tensorflow).

But while the Estimator API simplifies machine learning programming, it is not necessarily easy. And hence, I’ve written a 28-page tutorial to teach you how to use it from the ground up.

We start out with the Sci-Kit Learn API, and gradually build on those ideas to familiarize ourselves with the new Estimator API.

We go through a FULL CODE example on a dataset NEVER-BEFORE SEEN in my courses. Some new data wrangling techniques will be taught, in particular: the “hashing trick” and how to create embeddings instead of one-hot encoding categorical variables.

To get the VIP version of Modern Deep Learning in Python, just go here: https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=IAMAVIP2

[Note: make SURE you use the coupon code IAMAVIP2 – those who do not use the code will not get the VIP material!]

 

Now I realize that a lot of you might already be signed up for this course. That means, you get all the updated content for free and have been seeing the updates come through as I’ve added them. Woohoo!

But how can you get access to the VIP material?

If you support the work I’ve been doing on adding many hours of free updates to the courses you’ve already purchased, then consider signing up for the course at https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow

I’ve temporarily decreased the price to $10. Courses on deeplearningcourses.com will always contain the extra VIP material.

You can think of it like a small donation as a token of appreciation, but don’t forget you are still getting this 28-PAGE easy-to-follow tutorial on the Estimator API. Believe me, I’ve looked at other resources out there – they were not fun reading.

So, if you are already a student of Modern Deep Learning in Python and you would like to access the VIP stuff, get it now! This price won’t last.

 

To be sure: I still have TONS of stuff I still want to add to this course and my other courses. So much that sometimes I wonder how I’m going to get it all done! If you support this effort and you want to see MORE of it in the future, please do consider getting the VIP version of this course. I am very thankful for all the support!

Note: if you order the VIP version of the course through Udemy, you should receive a link to the VIP material within 24 hours of purchase in your message inbox. So don’t forget to check your messages! Shoot me a message if you haven’t got your VIP material by that time.

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Deep Learning Halloween SALE! 90% OFF ALL Udemy Courses

October 29, 2017

Huge updates ahead

I’ve been really busy adding tons of free updates to my existing courses. You can scroll down to the very bottom to see what they are. But in the mean time we are going to do another HUGE sale. ALL courses on Udemy are now $12. Take this opportunity to grab as many courses as you can because you never know when the next sale is going to be!

As usual, I’m providing $12 coupons for all my courses in the links below. Please use these links and share them with your friends!

You can also just type in the coupon code “OCT456”.

The promo goes until October 31. Don’t wait!

At the end of this post, I’m going to provide you with some additional links to get machine learning prerequisites (calculus, linear algebra, Python, etc…) for $12 too!

But that’s not all… I’m the Lazy Programmer, not just the Lazy Data Scientist – I’ve got $12 coupons for iOS development, Android development, Ruby on Rails, Python, Big Data / Hadoop / Spark, React.js, Angular, and MORE. All important skillsets on ANY engineering team. Got any friends or coworkers in mobile / backend / big data development? Let them know!

If you don’t know what order to take the courses in, please check here: https://deeplearningcourses.com/course_order

Deep Learning: GANs and Variational Autoencoders
https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/?couponCode=OCT456
Advanced AI: Deep Reinforcement Learning in Python
https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=OCT456
Artificial Intelligence: Reinforcement Learning in Python
https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=OCT456
Bayesian Machine Learning in Python: A/B Testing
https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=OCT456
Cluster Analysis and Unsupervised Machine Learning in Python
https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=OCT456
Deep Learning: Convolutional Neural Networks in Python
https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=OCT456
Deep Learning in Python
https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=OCT456
Practical Deep Learning in Theano and TensorFlow
https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=OCT456
Advanced Natural Language Processing: Deep Learning in Python
https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=OCT456
Ensemble Machine Learning in Python: Random Forest and AdaBoost
https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=OCT456
Unsupervised Machine Learning: Hidden Markov Models in Python
https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=OCT456
Deep Learning Prerequisites: Linear Regression in Python
https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=OCT456
Deep Learning Prerequisites: Logistic Regression in Python
https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=OCT456
Data Science: Natural Language Processing in Python
https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=OCT456
Deep Learning: Recurrent Neural Networks in Python
https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=OCT456
SQL for Newbs and Marketers
https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=OCT456
Data Science: Supervised Machine Learning in Python
https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=OCT456
Unsupervised Deep Learning in Python
https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=OCT456
 

 

 

PREREQUISITE COURSE COUPONS

And just as important, $12 coupons for some helpful prerequisite courses. You NEED to know this stuff before you study machine learning:

General (site-wide): http://bit.ly/2oCY14Z
Python http://bit.ly/2pbXxXz
Calc 1 http://bit.ly/2okPUib
Calc 2 http://bit.ly/2oXnhpX
Calc 3 http://bit.ly/2pVU0gQ
Linalg 1 http://bit.ly/2oBBir1
Linalg 2 http://bit.ly/2q5SGEE
Probability (option 1) http://bit.ly/2prFQ7o
Probability (option 2) http://bit.ly/2p8kcC0
Probability (option 3) http://bit.ly/2oXa2pb
Probability (option 4) http://bit.ly/2oXbZSK

 

 

 

OTHER UDEMY COURSE COUPONS

As you know, I’m the “Lazy Programmer”, not just the “Lazy Data Scientist” – I love all kinds of programming!

And I’ve got sales for everything:

iOS courses:
https://lazyprogrammer.me/ios

Android courses:
https://lazyprogrammer.me/android

Ruby on Rails courses:
https://lazyprogrammer.me/ruby-on-rails

Python courses:
https://lazyprogrammer.me/python

Big Data (Spark + Hadoop) courses:
https://lazyprogrammer.me/big-data-hadoop-spark-sql

Javascript, ReactJS, AngularJS courses:
https://lazyprogrammer.me/js

 

 

 

EVEN MORE COOL STUFF

Into Yoga in your spare time? Photography? Painting? There are courses, and I’ve got coupons! If you find a course on Udemy that you’d like a coupon for, just let me know and I’ll hook you up!

 

 

 

COURSE UPDATES

Recent updates to existing courses because my students are awesome and deserve free stuff:

  • Deep Learning pt 2: More lectures on hyperparameter optimization
  • Deep Learning pt 2: Keras!!! (very popular request)
  • Deep Learning pt 2: Noise injection
  • Deep Learning pt 1: Why Learn the Ins and Outs of Backpropagation?
  • All relevant courses: How to uncompress a .tar.gz file

Remember, these links will self-destruct on October 31 (4 days). Act NOW!

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Deep Learning: The Swish Activation Function

October 18, 2017

The Google Brain team has just released a new paper (https://arxiv.org/abs/1710.05941) that demonstrates the superiority of a new activation function called Swish on a number of different neural network architectures.

This is interesting because people often ask me, “which activation function should I use?”

These days, it is common to just use the ReLU by default.

To refresh your memory, the ReLU looks like this:

relu

And it is defined by the equation:

$$ f(x) = max(0, x) $$

One major problem with the ReLU is that its derivative is 0 for half the values of the input \( x \). Because we use “gradient descent” as our parameter update algorithm, if the gradient is 0 for a parameter, then that parameter will not be updated!

In other words, when I do:

$$ \theta = \theta – \alpha \frac{\partial J}{\partial \theta } $$

And:

$$ \frac{\partial J}{\partial \theta } = 0 $$

Then my update is just:

$$ \theta = \theta $$

Which just assigns the parameter back to itself.

This leads to the problem of “dead neurons”. Experiments have shown that neural networks trained with ReLUs can have up to 40% dead neurons!

There have been some proposed alternatives to this, such as the leaky ReLU, the ELU, and the SELU.

Interestingly, none of these have seemed to catch on and it’s still ReLU by default.

 

 

So how does the Swish activation function work?

The function itself is very simple:

$$ f(x) = x \sigma(x) $$

Where \( \sigma(x) \) is the usual sigmoid activation function.

$$ \sigma(x) = (1 + e^{-x})^{-1} $$

It looks like this:

Screen Shot 2017-10-18 at 2.39.55 PM

What’s interesting about this is that unlike every other activation function, it is not monotonically increasing. Does it matter? It seems the answer is no!

The derivative looks like this:

Screen Shot 2017-10-18 at 3.29.34 PM

One interesting thing we can do is re-parameterize the Swish, in order to “stretch out” the sigmoid:

$$ f(x) = 2x \sigma(\beta x) $$

We can see that, if \( \beta = 0 \), then we get the identity activation \( f(x) = x \), and if \( \beta \rightarrow \infty \) then the sigmoid converges to the unit step and multiplying that by \( x \) gives us back \( f(x) = 2 max(0, x) \) which is just the ReLU multiplied by a constant factor.

So including \( \beta \) is a way for us to nonlinearly interpolate between identity and ReLU.

The title of the paper is “A Self-Gated Activation Function”, which might make you wonder, “Why is it self-gated?”

This should remind you of the LSTM, where we have “gates” in the form of sigmoids that control how much of a vector gets passed on to the next stage, by multiplying it between the output of the sigmoid, which is a number between 0 and 1.

So “self-gated” means that the gate is just the sigmoid of the activation itself.

Gate: \( \sigma(x) \)

Value to pass through: \( x \)

But that’s enough theory. For most of us, we want to know: “Does it work?”

And more practically, “Can I just use this by default instead of the ReLU?”

The best thing to do is just to try it for yourself and see how robust it is to different settings of hyperparameters (learning rate, architecture, etc.) but let’s look at some results so we can be confident when it comes to using Swish:

Screen Shot 2017-10-18 at 3.42.46 PM

Click on the image to see it in the original size.

To compare Swish with baseline, a statistical test called the one-sided paired sign test was used.

Conclusion: Try Swish for yourself!

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Python 2-to-3 Tips

October 17, 2017

This is a short post to help those of you who need help translating code from Python 2 to Python 3.

Python 2 is the most popular Python version (at least at this time and certainly at the time my courses were created), hence why it was used.

It comes with Mac OS and Ubuntu pre-installed so when you type in “python” into your command line, you get Python 2.

This list is not exhaustive. It shows only code that appears commonly in my machine learning scripts, to assist the students taking my machine learning courses (https://deeplearningcourses.com).

 

Integer Division

OLD:

a / b

NEW:

a // b

 

For Loops

OLD:

for i in xrange

NEW:

for i in range

 

Printing

OLD:

print "hello world"

NEW:

print("hello world")
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