# New Course! Cutting-Edge AI: Deep Reinforcement Learning in Python

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.

# [New Release] Machine Learning and AI: Support Vector Machines in Python

January 22, 2019

### Support Vector Machines in Python

Wow, I didn’t think I’d be coming out with another course so soon – but here it is!

[By the way, I went all-out this time in the VIP version – you’ll want to check it out below – comes with 4 all-new models (both theory+code provided of course)]

SVMs are one of the most robust and powerful machine learning models. It can be a very useful “plug-and-play” solution – just throw your data in the model and wait for the magic to happen.

Unlike deep learning, where you can spend days or weeks tuning your hyperparameters, SVMs only have 2 hyperparameters, which are generally easy to understand and reason about.

One of the things you’ll learn about in this course is that a support vector machine actually is a neural network, and they essentially look identical if you were to draw a diagram.

The toughest obstacle to overcome when you’re learning about support vector machines is that they are very theoretical. This theory very easily scares a lot of people away, and it might feel like learning about support vector machines is beyond your ability. Not so!

In this course, we take a very methodical, step-by-step approach to build up all the theory you need to understand how the SVM really works. We are going to use Logistic Regression as our starting point, which is one of the very first things you learn about as a student of machine learning. So if you want to understand this course, just have a good intuition about Logistic Regression, and by extension have a good understanding of the geometry of lines, planes, and hyperplanes.

This course will cover the critical theory behind SVMs:

• Linear SVM derivation
• Hinge loss (and its relation to the Cross-Entropy loss)
• Quadratic programming (and Linear programming review)
• Slack variables
• Lagrangian Duality
• Kernel SVM (nonlinear SVM)
• Polynomial Kernels, Gaussian Kernels, Sigmoid Kernels, and String Kernels
• Learn how to achieve an infinite-dimensional feature expansion
• SMO (Sequential Minimal Optimization)
• RBF Networks (Radial Basis Function Neural Networks)
• Support Vector Regression (SVR)
• Multiclass Classification

As a VIP bonus, you will also get material for how to apply the “Kernel Trick” to other machine learning models. This is how you can use a model which is normally “weak” (such as linear regression) and make it “strong”. I’ve chosen models from various different areas of machine learning.

• Kernel Linear regression (for regression)
• Kernel Logistic regression (for classification)
• Kernel K-means clustering (for clustering)
• Kernel Principal components analysis (PCA) (for dimensionality reduction)

Remember – the VIP bonus is only available at https://deeplearningcourses.com/c/support-vector-machines-in-python.

See here what linear regression can be capable of:

And logistic regression:

When the kernel trick is applied!

For those of you who are thinking, “theory is not for me”, there’s lots of material in this course for you too!

In this course, there will be not just one, but two full sections devoted to just the practical aspects of how to make effective use of the SVM.

We’ll do end-to-end examples of real, practical machine learning applications, such as:

• Image recognition
• Spam detection
• Medical diagnosis
• Regression analysis

For more advanced students, there are also plenty of coding exercises where you will get to try different approaches to implementing SVMs.

These are implementations that you won’t find anywhere else in any other course.

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.

# New Years 2019

### How to meet your New Years resolutions in 2019

Firstly, I’d like to wish everyone on this list a happy new year, we are off to a great start. The new year is a time to set goals, turn things around, and be better than we were before.

What better way than to learn from thousands of experts around the world who are the best at what they do? Luckily, I’ve got something that will make it just a little easier.

I know a lot of you have been waiting for this – well here it is – the LOWEST price possible on ALL Udemy courses (yes, the whole site!)

For the next 10 days, ALL courses on Udemy (not just mine) are available for just $9.99! For my courses, please use the Udemy coupons below (included in the links below), or if you want, enter the coupon code: JAN2019. For prerequisite courses (math, stats, Python programming) and all other courses (Bitcoin, meditation, yoga, guitar, photography, whatever else you want to learn), follow the links at the bottom (or go to my website). Since ALL courses on Udemy are on sale, if you want any course not listed here, just click the general (site-wide) link, and search for courses from that page. https://www.udemy.com/recommender-systems/?couponCode=JAN2019 https://www.udemy.com/deep-learning-advanced-nlp/?couponCode=JAN2019 ### 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/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:

Javascript, ReactJS, AngularJS 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!

# Artificial Intelligence Boxing Day Blowout!

December 26, 2018

#### Deep Learning and AI Courses for just $11.99 # Boxing Day 2018 ### Celebrate the Holidays with New AI & Deep Learning Courses! I’ve been busy making free content and updates for my existing courses, so guess what that means? Everything on sale! For the next week, all my Deep Learning and AI courses are available for just$11.99!

For my courses, please use the coupons below (included in the links), or if you want, enter the coupon code: DEC2018.

For prerequisite courses (math, stats, Python programming) and all other courses, follow the links at the bottom for sales of up to 90% off!

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

https://www.udemy.com/recommender-systems/?couponCode=DEC2018

# Black Friday 2018

### Udemy’s BIGGEST Sale of the YEAR is back!

I know a lot of you have been waiting for this – well here it is – the LOWEST price possible on ALL Udemy courses (yes, the whole site!)

For the next 7 days, ALL courses on Udemy (not just mine) are available for just $9.99! For my courses, please use the coupons below (included in the links below), or if you want, enter the coupon code: NOV2018. For prerequisite courses (math, stats, Python programming) and all other courses (yoga, guitar, photography, whatever else you want to learn), follow the links at the bottom. Since ALL courses on Udemy are on sale, if you want any course not listed here, just click the general (site-wide) link, and search for courses from that page. https://www.udemy.com/recommender-systems/?couponCode=NOV2018 ### 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/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:

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!

# NEW course! Recommender Systems and Deep Learning in Python

September 13, 2018

### Recommender Systems and Deep Learning in Python

So excited to tell you about my new course!

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.

YouTube: Video dashboard (and recommendations to the right of every video)

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 RedditHacker 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 AmazonNetflix, 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.

# Special Announcment: Deep Learning Keras Book!

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!

# “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!

# 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.

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.

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.

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

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!