Tensorflow 2.0 is here!

I am happy to announce my latest and most massive course yet – Tensorflow 2.0: Deep Learning and Artificial Intelligence.

Guys I am not joking – this really is my most massive course yet – check out the curriculum.

Many of you will be interested in the stock prediction example, because you’ve been tricked by marketers posing as data scientists in the past – I will demonstrate why their results are seriously flawed.

[if you don’t want to read my little spiel just click here to get your VIP coupon: https://www.udemy.com/deep-learning-tensorflow-2/?couponCode=TENSORVIP]

This is technically Deep Learning in Python part 12, but importantly this need not be the 12th deep learning course of mine that you take!

There are quite few important points to cover in this announcement, so let me outline what I will discuss:

A) What’s covered in this course
B) Why there are almost zero prerequisites for this course
C) The VIP content and near-term additions
D) The story behind this course (if you’ve been following my courses for some time you will be interested in this)

What’s covered in this course

As mentioned – this course is massive. It’s going to take you from basic linear models (the neuron) to ANNs, CNNs, and RNNs.

Thanks to the new standardized Tensorflow 2.0 API – we can move quickly.

The theme of this course is breadth, not depth. If you’re looking for heavy theory (e.g. backpropagation), well, I already have courses for those. So there’s no point in repeating that.

We will however go pretty in-depth to ensure that convolution (the main component of CNNs) and recurrent units (the main component of RNNs) are explained intuitively and from multiple perspectives.

These will include explanations and intuitions you have likely not seen before in my courses, so even if you’ve taken my CNN and RNN courses before, you will still want to see this.

There are many applications in this course. Here are a few:

– we will prove Moore’s Law using a neuron
– image classification with modern CNN design and data augmentation
– time series analysis and forecasting with RNNs

Anyone who is interested in stock prediction should check out the RNN section. Most RNN resources out there only look at NLP (natural language processing), including my old RNN course, but very few look at time series and forecasting.

And out of the ones that do, many do forecasting totally wrong!

There is one stock forecasting example I see everywhere, but its methodology is flawed. I will demonstrate why it’s flawed, and why stock prediction is not as simple as you have been led to believe.

There’s also a ton of Tensorflow-specific content, such as:

– Tensorflow serving (i.e. how to build a web service API from a Tensorflow model)
– Distributed training for faster training times (what Tensorflow calls “distribution strategies”)
– Low-level Tensorflow – this has changed completely from Tensorflow 1.x
– How to build your own models using the new Tensorflow 2.0 API
– Tensorflow Lite (how to export your models for mobile devices – iOS and Android) (coming soon)
– Tensorflow.js (how to export your models for the browser) (coming soon)

Why there are almost zero prerequisites for this course

Due to the new standardized Tensorflow 2.0 API, writing neural networks is easier than ever before.

This means that we’ll be able to blast through each section with very little theory (no backpropagation).

All you will need is a basic understanding of Python, Numpy, and Machine Learning, which are all taught in my free Numpy course.

As I always say, it’s free, so you have no excuses!

Tensorflow 2.0 however, does not invalidate or replace my other courses. If you haven’t taken them yet, you should take this course first for breadth, and then take the other courses which focus on individual models (CNNs, RNNs) for depth.

The VIP content and near-term additions

I had so much content in mind for this course, but I wanted to get this into your hands as soon as possible. With Tensorflow 2.0 due to be released any day now, I wanted to give you all a head start.

This field is moving so fast things were changing while I was making the course. Insane!

I’ll be adding more content in the coming weeks, possibly including but not limited to:

– Transfer Learning
– Natural Language Processing
– GANs
– Recommender Systems
– Reinforcement Learning

For this release, only the VIP version will be available for some time. That is why you do not see the usual Udemy discount.

You may be wondering: Which parts of the content are VIP content, and which are not?

This time, I wanted to do something interesting: it’s a surprise!

The VIP content will be added to a special section called the “VIP Section”, and this will be removed once the course becomes “Non-VIP”.

I will make an announcement well before that happens, so you will have the chance to download the VIP content before then, as well as get access to the VIP content permanently from deeplearningcourses.com.

The story behind this course

Originally, this course was going to be an RNN course only (hence why the RNN sections have so much more content – both time series and NLP).

The reason for this was, my original RNN course was tied to Theano and building RNNs from scratch.

In Tensorflow, building RNNs is completely different. This is unlike ANNs and CNNs which are relatively similar.

Thus, I could never reconcile the differences between the Theano approach and the Tensorflow approach in my original RNN course. So, I decided that simply making a new course for RNNs in Tensorflow would be best.

But lo and behold – Tensorflow was evolving so fast that a new version was about to be released – so I thought, it’s probably best to just cover everything in Tensorflow 2.0!

And that is how this current course came to be.

I hope you enjoy this action-packed course.

I’ll see you in class!

AI / Machine Learning Summer Sale

For the next week, all my Deep Learning and AI courses are available for just $9.99! (In addition to other courses on the site for the next few days)

For those of you who have been around for some time, you know that this sale doesn’t come around very often – just a few times per year. If you’ve been on the fence about getting a course, NOW is the time to do so. Get it now – save it for later.

For my courses, please use the coupons below (included in the links), or if you want, enter the coupon code: JUN2019.

As usual, if you want to know what order to take my courses in, check out the lecture “What order should I take your courses in?” in the Appendix of any of my courses (including the free Numpy course).

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.

[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.

Support Vector Machines in Python

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

[if you don’t want to read my little spiel just click here to get your VIP coupon: https://deeplearningcourses.com/c/support-vector-machines-in-python]

[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
• Projected Gradient Descent
• 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.

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.