September 13, 2018

Recommender Systems and Deep Learning in Python So excited to tell you about my new course! [if you don’t want to read my little spiel just click here to get your coupon: https://www.udemy.com/recommender-systems/?couponCode=LAUNCHDAY] Believe it or not, almost all online businesses today make use of recommender systems in some way or another. What do I mean by “recommender systems”, and why are they useful? Let’s look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook. Recommender systems form the very foundation of these technologies. Google: Search results (why Google is a billion dollar company!) YouTube: Video dashboard (and recommendations to the right of every video) Facebook: News feed This course is a big bag of tricks that make recommender systems work across multiple platforms. We’ll look at popular news feed algorithms, like Reddit, Hacker News, and Google PageRank. We’ll look at Bayesian recommendation techniques that are being used by a large number of media companies today.   But this course isn’t just about news feeds. Companies like Amazon, Netflix, and Spotify have been using recommendations to suggest products, movies, and music to customers for many years now. These algorithms have led to billions of dollars in added revenue. So I assure you, what you’re about to learn in this course is very real, very applicable, and will have a huge impact on your business.   For those of you who like to dig deep into the theory to understand how things really work, you know this is my specialty and there will be no shortage of that in this course. We’ll be covering state of the art algorithms like matrix factorization and deep learning (making use of both supervised andunsupervised learning), and you’ll learn a bag full of tricks to improve upon baseline results.   Whether you sell products in your e-commerce store, or you simply write a blog – you can use these techniques to show the right recommendations to your users at the right time. If you’re an employee at a company, you can use these techniques to impress your manager and get a raise!   I’ll see you in class!   GET THE COURSE NOW Note: this course is NOT a part of my deep learning series (it’s not Deep Learning part 11) because while it contains a major deep learning component, a lot of the course uses non-deep learning techniques as well. The deep learning parts apply modified neural network architectures and deep learning technologies to the recommender problem.

September 12, 2018

Simple Deep Learning for Programmers

Learn Deep Learning via Keras examples with absolutely no math I’m always intrigued when students tell me they want to learn deep learning without doing any math. I was explaining to someone just yesterday – if you look at <insert famous deep learning book by famous deep learning researcher here> – the entire thing is actually cover to cover equations. Ha! Anyhow, I wanted to test this hypothesis. How far can one get, if they try to learn deep learning via an API? So I made this little book. It’s full of Keras examples, starting from a basic feedforward neural network, then adding some modern techniques like dropout and batch norm, then moving to more advanced architectures like CNNs and RNNs. Of course, if you are a reader of my newsletter, you probably aren’t afraid of math! But, I thought I’d share this book with you anyway, since it contains some interesting examples that you haven’t seen in my courses before. – CIFAR dataset – time series prediction using an RNN – machine translation using a Bidirectional RNN (not a seq-to-seq model as in my Advanced NLP course) This would also be a great opportunity to brush up on your Keras skills, which are going to be useful for my next course (hopefully coming out in a few days!) Finally – I’ve also linked below my related book, “Simple Machine Learning for Programmers” – it is a similar experiment in teaching about machine learning using an API with no math. It’s the same as the machine learning section of my Numpy course but I know some students like to have written versions of things so they can read on the subway / airplane. If so, check it out! Get the book now

July 15, 2018

https://www.udemy.com/deep-learning-advanced-nlp/?couponCode=JULY2018 https://www.udemy.com/advanced-computer-vision/?couponCode=JULY2018 https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/?couponCode=JULY2018 https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=JULY2018 https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=JULY2018 https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=JULY2018 https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=JULY2018 https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=JULY2018 https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=JULY2018 https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=JULY2018   https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=JULY2018   https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=JULY2018   https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=JULY2018   https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=JULY2018   https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=JULY2018   https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=JULY2018   https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=JULY2018   https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=JULY2018   https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=JULY2018   https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=JULY2018   PREREQUISITE COURSE COUPONS 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): 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!   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!

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?

UPDATES FOR BEGINNERS:

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.

UPDATES FOR INTERMEDIATE STUDENTS:

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.

UPDATES FOR EXPERTS:

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!

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!

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!

THERE’S MORE:

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.

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
https://deeplearningcourses.com/c/deep-learning-advanced-nlp

NOTES:

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!

April 11, 2018

ALL Courses on Udemy $10.99

April 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 5 days, ALL courses on Udemy (not just mine) are available for just $10.99!

For my courses, please use the coupons below (included in the links), or if you want, enter the coupon code: APR2018.

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

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PREREQUISITE COURSE COUPONS

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

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Python http://bit.ly/2pbXxXz
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Calc 2 http://bit.ly/2oXnhpX
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iOS courses:
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Android courses:
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Ruby on Rails courses:
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Python courses:
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Big Data (Spark + Hadoop) courses:
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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!

April 9, 2018

https://medium.com/tensorflow/introducing-tensorflow-hub-a-library-for-reusable-machine-learning-modules-in-tensorflow-cdee41fa18f9

March 17, 2018

ALL Courses on Udemy $10.99

St. Patrick’s Day 2018

Who needs luck when you have machine learning?

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

For the next 3 days, ALL courses on Udemy (not just mine) are available for just $10.99!

For my courses, please use the coupons below (included in the links), or if you want, enter the coupon code: MAR2018.

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

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PREREQUISITE COURSE COUPONS

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
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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!

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!

February 19, 2018

Deep Learning: Advanced Computer Vision

[Scroll down to the bottom for the coupon and important instructions for how to use it]

This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years.

When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks.

I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.

Let me give you a quick rundown of what this course is all about:

We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG, ResNet, and Inception (named after the movie which by the way, is also great!)

We’re going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots.

In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label.

You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)

We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors.

Another very popular computer vision task that makes use of CNNs is called neural style transfer.

This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds.

AWESOME FACTS:

• One of the major themes of this course is that we’re moving away from the CNN itself, to systems involving CNNs.
• Therefore, instead of focusing on the detailed inner workings of CNNs (which we’ve already done), we’ll focus on high-level building blocks. The result? Almost zero math.
• Another result? No complicated low-level code such as that written in Tensorflow, Theano, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.

The VIP Version

For this launch, I am offering a limited edition VIP version of the course. This offer will END in exactly 5 days (before next weekend!).

As usual, you MUST use the coupon IAMAVIP (automatically applied when you use the link below) to get the VIP version. If you do not use the IAMAVIP coupon, you will not get the VIP bonuses.

So, what do you get with the VIP version?

The final section of the course is on neural style transfer – however – a brand new section – one I may not release for a long time, if ever, is in the works.

This “hidden section” is on super resolution and fast neural style transfer (speeding up the original neural style transfer algorithm).

Super resolution is where you take a low-quality, small image, and turn it into a higher-quality, higher resolution image. It’s the stuff of science fiction and spy movies – now it’s real!

This new hidden section of the course will be provided to you in the form of a book chapter (PDF format) along with accompanying code which will NOT appear in the official course repo.

To get it, use the coupon below (automatically applied when you click the link):

https://www.udemy.com/advanced-computer-vision/?couponCode=IAMAVIP

Note: Please allow up to 72h for delivery of your VIP bonus.

Important!!!: Users have reported that the IAMAVIP coupon code may get overridden by Udemy’s own codes. When you checkout, you’ll want to make note of something like this:

Notice how it says “IAMAVIP is not applied”. As far as I know, there’s no way around this problem. However, note that you can ALWAYS get VIP material by signing up for the course at https://deeplearningcourses.com/c/advanced-computer-vision. If you purchase the course at deeplearningcourses.com, and you’d like to access the course on Udemy as well, just shoot me an email and I will give you a free coupon.