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!
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.
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!
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!
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!
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!
[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):
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.
In 2018 and beyond, AI will continue to rise in importance, and the best jobs, the best new technologies – will absolutely depend on AI to have a competitive edge in the marketplace.
For the next 2 days, ALL courses on Udemy are available for just $10.99! (My courses will stay on sale for at least a week longer)
For my courses, please use the coupons below (included in the links), or if you want, enter the coupon code yourself: LUNAR2018.
For prerequisite courses and all other courses, follow the links at the bottom.
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!
Last Chance to meet your 2018 New Years Resolutions!
Earlier this year we had a New Years sale with all courses 90% off. I’ve been getting a lot of emails from those who missed it, those who just joined, etc. Don’t worry – you have one more chance to meet your New Years 2018 goals!
In 2018 and beyond, AI will continue to rise in importance, and the best jobs, the best new technologies – will absolutely depend on AI to have a competitive edge in the marketplace.
For the next 2 days, Udemy is extending its New Years sale, and ALL courses on the site 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: BLASTOFF2018.
For prerequisite courses and all other courses, follow the links at the bottom.
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!
