“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!

Go to comments

HUGE UDEMY SALE: All Courses $9.99

May 7, 2018

ALL Courses on Udemy $9.99

May 2018

Grab these courses before these sales go away

I’m hard at work at my next course, so guess what that means? Everything on sale!

For the next 7 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: MAY2018.

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.







































And just as important, $10.99 coupons for some helpful prerequisite courses. You NEED to know this stuff to understand machine learning in-depth:

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



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!

Go to comments

NEW Deep Learning Course: Advanced NLP and RNNs

May 1, 2018


Over the past year, many of you have been asking for a followup on my RNN and Deep NLP courses. I am glad to announce that today, that course is here.

Deep Learning: Advanced NLP and RNNs

I decided to combine both NLP (natural language processing) and RNNs (recurrent neural networks) because these topics are so intertwined it’s almost impossible to talk about one without the other.

In recent years, a few ideas have started to bubble up and have shown themselves to be truly useful, and in this course, I bring those ideas to you.

Let’s start with the applications:

1. I’ve been asked quite a few times about how to do classification when each input can have multiple labels assigned to it. We will do a text classification problem that has data exactly like this.

2. Neural machine translation. One of the most popular applications of Deep NLP. We can’t not do this.

3. Question answering. You can think of this as “reading comprehension”. Can an AI read a story and answer a question about it? Facebook Research made this popular with their bAbI dataset.

4. Speech recognition (see below).

As you know I like to take an abstract view of machine learning. We know that all of the techniques for these applications can be used for yet more applications without any change in code because the “data is the same”. For example, a spam detection dataset looks no different than a sentiment analysis dataset.

In the same vein, neural machine translation is no different from simple versions of question answering and chatbots. So you are really learning how to do all of these things at the same time.

We will of course get a chance to review basics such as LSTMs, GRUs, language modeling, word embeddings, and so forth.

What techniques will we cover? These techniques are what have helped RNNs really work well for NLP in the recent past:

1. Bidirectional RNNs

2. Sequence-to-sequence models (seq2seq)

3. Attention

4. Memory networks


So, if you’ve already heard about these and you wanted to learn about them – I hope you are excited!




This course is NOT just about RNNs but CNNs (convolutional neural networks) as well. This is an advanced course – ALL deep learning is fair game.

Early in the course, you’ll see how we can apply CNNs to text.

You will see that we get results on-par with LSTMs and GRUs.

That’s already pretty neat.

But there’s still more.

If you’re reading this, you automatically get access to the VIP version of the course, which contains EVEN MORE material.

For the first time, I’m releasing a course exclusively on https://deeplearningcourses.com

This course will appear on other sites in the future but you will NOT get the VIP version from those sites.

What’s in the VIP bonus?

It’s basically like an entirely new section of the course.

We will be looking at a topic I’ve wanted to cover for a long time: speech recognition.


Unlike the usual type of NLP stuff which focuses on text, speech recognition focuses on audio.

Text is neat and formatted. When you type the word “the” it’s the same as if I type the word “the”.

The same cannot be said for audio. When you say “the” it sounds different from when I say “the”.

Audio is a real-world, physical signal like images are.

In that sense, speech recognition is more like computer vision.

In fact, you’ll see how we can apply CNNs to this task as well.

I love this section of the course because we get to dive into some very cool, never-before-seen material in order to do speech processing – namely time-series techniques such as the Fourier transform.

Screen Shot 2018-05-01 at 1.19.20 AM

You’ll even get a brief glimpse into how the Fourier transform is related to quantum mechanics and Heisenberg’s uncertainty principle!

Enough talk. Get the course here:

Deep Learning: Advanced NLP and RNNs


1. As usual, if you purchase the course on deeplearningcourses.com and you’d like access on Udemy as well, I will do that for you once the course is released there.

2. I’ve made a lot of updates to deeplearningcourses.com recently, so hopefully you find them useful! Always happy to consider feature requests.

3. I recently moved deeplearningcourses.com to a shiny new server, so if you have any problems, please let me know. Everything seems to be running smoothly so far!

Go to comments

Deep Learning and Artificial Intelligence Newsletter

Get discount coupons, free machine learning material, and new course announcements