May 9, 2019

Quite a few of you have been asking when I’d do another Reinforcement Learning course… well, how about today? 😉 [if you don’t want to read my little spiel just click here to get your VIP coupon: https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence] This is technically Deep Learning in Python part 11, and my 3rd reinforcement learning course, which is super awesome. The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer. Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be. We’ve seen how AlphaZero can master the game of Go using only self-play. This is just a few years after the original AlphaGo already beat a world champion in Go. We’ve seen real-world robots learn how to walk, and even recover after being kicked over, despite only being trained using simulation. Simulation is nice because it doesn’t require actual hardware, which is expensive. If your agent falls down, no real damage is done. We’ve seen real-world robots learn hand dexterity, which is no small feat. Walking is one thing, but that involves coarse movements. Hand dexterity is complex – you have many degrees of freedom and many of the forces involved are extremely subtle. Last but not least – video games. Even just considering the past few months, we’ve seen some amazing developments. AIs are now beating professional players in CS:GO and Dota 2. So what makes this course different from the first two? Now that we know deep learning works with reinforcement learning, the question becomes: how do we improve these algorithms? This course is going to show you a few different ways: including the powerful A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolution strategies. Evolution strategies is a new and fresh take on reinforcement learning, that kind of throws away all the old theory in favor of a more “black box” approach, inspired by biological evolution. What’s also great about this new course is the variety of environments we get to look at. First, we’re going to look at the classic Atari environments. These are important because they show that reinforcement learning agents can learn based on images alone. Second, we’re going to look at MuJoCo, which is a physics simulator. This is the first step to building a robot that can navigate the real-world and understand physics – we first have to show it can work with simulated physics. Finally, we’re going to look at Flappy Bird, everyone’s favorite mobile game just a few years ago. What do you get if you sign up for the VIP version of this course? A brand new exclusive section covering an entirely new algorithm: TD3! As usual, both theory and code for this powerful state-of-the-art algorithm are provided. I’ll see you in class! P.S. As usual, if you primarily use another site (e.g. Udemy) you will automatically get free access (upon request) if you’ve already purchased the VIP version of the course from deeplearningcourses.com. Get the course now

January 1, 2019

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December 26, 2018

https://www.udemy.com/recommender-systems/?couponCode=DEC2018 https://www.udemy.com/deep-learning-advanced-nlp/?couponCode=DEC2018 https://www.udemy.com/advanced-computer-vision/?couponCode=DEC2018 https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/?couponCode=DEC2018 https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=DEC2018 https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=DEC2018 https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=DEC2018 https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=DEC2018 https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=DEC2018 https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=DEC2018 https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=DEC2018 https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=DEC2018 https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=DEC2018 https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=DEC2018 https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=DEC2018 https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=DEC2018 https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=DEC2018 https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=DEC2018 https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=DEC2018 https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=DEC2018 https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=DEC2018   PREREQUISITE COURSE COUPONS And just as important, $11.99 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/2p8kcC0 Probability (option 2) http://bit.ly/2oXa2pb Probability (option 3) 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!

November 14, 2018

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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

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

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!

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.

February 14, 2018

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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 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
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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!

January 17, 2018

Historically, Windows users have had the most trouble installing deep learning, machine learning, data science, and AI libraries.

Just a few years ago this process would have taken you hours, if not days, to complete, if you completed it at all (as opposed to just giving up).

Nowadays, things are MUCH easier.

In this lecture, I walk you through how to install the following libraries on Windows:

• Numpy
• Scipy
• Matplotlib
• Pandas
• NLTK (for NLP)
• Scikit-Learn
• Tensorflow
• Theano
• Keras
• PyTorch
• CNTK
• OpenAI Gym
• OpenAI Gym Atari Extension
• ffmpeg (needed to save videos in OpenAI Gym)