The complete PyTorch course has arrived
I hope you are all staying safe. Well, I’m sure you’ve heard enough about that so how about some different news?
Today, I am announcing the VIP version of my latest course: PyTorch: Deep Learning and Artificial Intelligence
[If you don’t want to read my little spiel just click here to get your VIP coupon: https://www.udemy.com/course/pytorch-deep-learning/?couponCode=PYTORCHVIP]
[The NEW VIP coupon for May 2 – June 2 2020 is: https://www.udemy.com/course/pytorch-deep-learning/?couponCode=PYTORCHVIP2]
[The NEW VIP coupon for June 2 – July 3 2020 is: https://www.udemy.com/course/pytorch-deep-learning/?couponCode=PYTORCHVIP3]
[The NEW VIP coupon for July 6 – August 6 2020 is: https://www.udemy.com/course/pytorch-deep-learning/?couponCode=PYTORCHVIP4]
[The NEW VIP coupon for August 7 – September 7 2020 is: https://www.udemy.com/course/pytorch-deep-learning/?couponCode=PYTORCHVIP5]
[The NEW VIP coupon for September 8 – October 8 2020 is: https://www.udemy.com/course/pytorch-deep-learning/?couponCode=PYTORCHVIP6]
https://www.udemy.com/course/pytorch-deep-learning/?couponCode=PYTORCHVIP7 (ends November 9, 2020)
https://www.udemy.com/course/pytorch-deep-learning/?couponCode=PYTORCHVIP8 (ends December 10, 2020)
This is a MASSIVE (over 22 hours) Deep Learning course covering EVERYTHING from scratch. That includes:
- Machine learning basics (linear neurons)
- ANNs, CNNs, and RNNs for images and sequence data
- Time series forecasting and stock predictions (+ why all those fake data scientists are doing it wrong)
- NLP (natural language processing)
- Recommender systems
- Transfer learning for computer vision
- GANs (generative adversarial networks)
- Deep reinforcement learning and applying it by building a stock trading bot
IN ADDITION, you will get some unique and never-before-seen VIP projects:
Estimating prediction uncertainty
Drawing the standard deviation of the prediction along with the prediction itself. This is useful for heteroskedastic data (that means the variance changes as a function of the input). The most popular application where heteroskedasticity appears is stock prices and stock returns – which I know a lot of you are interested in.
It allows you to draw your model predictions like this:
Sometimes, the data is simply such that a spot-on prediction can’t be made. But we can do better by letting the model tell us how certain it is in its predictions.
Facial recognition with siamese networks
This one is cool. I mean, I don’t have to tell you how big facial recognition has become, right? It’s the single most controversial technology to come out of deep learning. In the past, we looked at simple ways of doing this with classification, but in this section I will teach you about an architecture built specifically for facial recognition.
You will learn how this can work even on small datasets – so you can build a network that recognizes your friends or can even identify all of your coworkers!
You can really impress your boss with this one. Surprise them one day with an app that calls out your coworkers by name every time they walk by your desk. 😉
Please note: The VIP coupon will work only for the next month (ending May 1, 2020). It’s unknown whether the VIP period will renew after that time.
After that, although the VIP content will be removed from Udemy, all who purchased the VIP course will get permanent free access on deeplearningcourses.com.
This course is designed to be a beginner to advanced course. All that is required is that you take my free Numpy prerequisites to learn some basic scientific programming in Python. And it’s free, so why wouldn’t you!?
You will learn things that took me years to learn on my own. For many people, that is worth tens of thousands of dollars by itself.
There is no heavy math, no backpropagation, etc. Why? Because I already have courses on those things. So there’s no need to repeat them here, and PyTorch doesn’t use them. So you can relax and have fun. =)
All of my deep learning courses until now have been in Tensorflow (and prior to that Theano).
So why learn PyTorch?
Does this mean my future deep learning courses will use PyTorch?
In fact, if you have traveled in machine learning circles recently, you will have noticed that there has been a strong shift to PyTorch.
Case in point: OpenAI switched to PyTorch earlier this year (2020).
Major AI shops such as Apple, JPMorgan Chase, and Qualcomm have adopted PyTorch.
PyTorch is primarily maintained by Facebook (Facebook AI Research to be specific) – the “other” Internet giant who, alongside Google, have a strong vested interest in developing state-of-the-art AI.
But why PyTorch for you and me? (aside from the fact that you might want to work for one of the above companies)
As you know, Tensorflow has adopted the super simple Keras API. This makes common things easy, but it makes uncommon things hard.
With PyTorch, common things take a tiny bit of extra effort, but the upside is that uncommon things are still very easy.
Creating your own custom models and inventing your own ideas is seamless. We will see many examples of that in this course.
For this reason, it is very possible that future deep learning courses will use PyTorch, especially for those advanced topics that many of you have been asking for.
Because of the ease at which you can do advanced things, PyTorch is the main library used by deep learning researchers around the world. If that’s your goal, then PyTorch is for you.
In terms of growth rate, PyTorch dominates Tensorflow. PyTorch now outnumbers Tensorflow by 2:1 and even 3:1 at major machine learning conferences. Researchers hold that PyTorch is superior to Tensorflow in terms of the simplicity of its API, and even speed / performance!
Do you need more convincing?