Lazy Programmer

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Learn PyTorch Basics: New YouTube Playlist

August 2, 2018

I’ve finally gotten around to adding a section on PyTorch basics to my course, Modern Deep Learning in Python (which already goes in-depth on Theano and Tensorflow).

As you recall, this course focuses on modern deep learning techniques such as adaptive learning rates and momentum, modern deep learning frameworks and GPU acceleration, and modern regularization techniques like dropout and batch normalization.

Check out the new videos here:

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Artificial Intelligence SUMMER SALE – All courses $9.99!

July 15, 2018

ALL Courses on Udemy $9.99

July 2018


Too hot outside? Watch AI & Deep Learning videos instead!

I’ve been busy making free content and updates for my existing courses, so guess what that means? Everything on sale!

For the next 3 days, ALL courses on Udemy (not just mine) are available for just $9.99!

This is the lowest price possible on Udemy, so make sure you grab these courses while you have the chance.

For my courses, please use the coupons below (included in the links), or if you want, enter the coupon code: JULY2018.

For prerequisite courses (math, stats, Python programming) and all other courses, follow the links at the bottom.

Since ALL courses on Udemy on sale, for any course not listed here, just click the general (site-wide) link, and search for courses from that page.

BY THE WAY: Did you see my latest announcement about the massive updates I just made to my original Deep Learning with NLP course? It includes a brand new section called “Beginner’s Corner” which is designed to be useful for beginners to ML who aren’t quite ready for the rest of the course yet. If not: read more here.

ALSO: Got any requests? What do you want to learn about? (Doesn’t have to be Deep Learning or AI-related) Let me know!



And just as important, $9.99 coupons for some helpful prerequisite courses. You NEED to know this stuff to understand machine learning in-depth:

General (site-wide):
Calc 1
Calc 2
Calc 3
Linalg 1
Linalg 2
Probability (option 1)
Probability (option 2)
Probability (option 3)
Probability (option 4)



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


iOS courses:

Android courses:

Ruby on Rails courses:

Python courses:

Big Data (Spark + Hadoop) courses:

Javascript, ReactJS, AngularJS courses:



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!

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FREE Updates to NLP: Deep Learning for Beginners!

July 15, 2018


You may have noticed that my course Natural Language Processing with Deep Learning in Python has gotten a lot longer recently!

As part of my course revitalization process, I’ve added a significant number of updates to this course.

All students are receiving this announcement because no matter what skill-level you’re currently at, you will get a lot of value from this update.

What has changed?



A brand new section called “Beginner’s Corner”. This section requires only basic machine learning knowledge. Know what a feature vector is, and know how to use the SciKit-Learn API.

You will get a taste of what word2vec and GloVe vectors can do.

You can still get a good understanding of what the course is about even if you’re not (yet!) ready to tackle the rest of the course.



A brand new “Review” section has been added. This section focuses on the bigram model, and several ways to implement it.

1) Just counting. For example, p(heads) = # heads / # total.

2) A single neuron (logistic model). We show how this is equivalent to #1.

3) A neural network model. We show how this actually makes #2 more efficient.

Crucially, this section provides you with all the techniques you need to tackle the next section on word2vec.

The word2vec section has also been completely re-done in order to take advantage of the concepts learned in the Review, making the transition seamless.

Finally, a brand new section on word vectors unifies the word2vec and GloVe sections, giving you a totally new (and in my opinion, better) way of training word2vec.



Additional theory lectures and Tensorflow code have been added to the RNN and Recursive Neural Network sections. Recall: the latter is a neural network structured like a tree.

Yes, Tensorflow’s capabilities have caught up! We can now do everything in Tensorflow that we’d previously done in Theano.

So if you’ve been avoiding these sections because you didn’t know Theano, now you have no excuse. =)


Click HERE to get the course if you don’t have it yet.

Hope you enjoy the updates, happy learning!

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t-SNE in the wild: Scientists have captured the elusive cell that can regenerate an entire flatworm

June 17, 2018

Link here:

First of all, this is a really cool finding for any of you who are into longevity research.

Secondly, this is a really cool example of a project that incorporates some machine learning, but also some hand-derived rules using domain expertise.

Basically, the researchers took a set of stem cells, then studied the properties of those cells. At each stage, they grouped the cells based on whether or not those cells would be capable of regenerating the flatworm. Finally, they ended up with the relevant cell.

(You might think of that as a decision tree)

At one stage, they use t-SNE to visualize clusters of different types of cells:


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“To all you that are trying to tell people they can become professionals in just a few weeks JUST to sell your product – shame on you!”

May 21, 2018

This is a great video that explains a lot of what I’ve observed from students trying to machine learning, but put more eloquently than I could have said myself. =)

I’m always having to contend against students who have taken a super easy-peasy course, actually learned nothing, but believe they know everything. Then, when they come up against the real content, they believe it’s because the instructor is trying to make the course really “elite” or trying to make them feel “dumb” by including lots of math and/or programming that they can’t understand.

But realize:

  • I (or any other instructor) did not invent these subjects
  • If the subject requires math, that’s because it does
  • If the subject requires programming, that’s because it does

We didn’t put math in there just to torture you. If you’re taking a math course, it’s probably going to have math in it.

A student gets frustrated because they don’t understand the real subject, but really they should be frustrated with the instructor who gave them the empty course that provided them with no skill and too much confidence.

This video is about software developers, but if you view it from the perspective of machine learning, everything still applies. Watch the video!

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