March 3, 2020
Hello deep learning and AI enthusiasts!
As we all know, the near future is somewhat uncertain. With an invisible virus spreading around the world at an alarming rate, some experts have suggested that it may reach a significant portion of the population.
Schools may close, you may be ordered to work from home, or you may want to avoid going outside altogether. This is not fiction – it’s already happening.
There will be little warning, and as students of science and technology, we should know how rapidly things can change when we have exponential growth (just look at AI itself).
Have you decided how you will spend your time?
I find moments of quiet self-isolation to be excellent for learning advanced or difficult concepts – particularly those in machine learning and artificial intelligence.
To that end, I’ll be releasing several coupons today – hopefully that helps you out and you’re able to study along with me.
Modern Deep Learning in Python
Despite the fact that I just released a huge course on Tensorflow 2, this course is more relevant than ever. You might take a course that uses batch norm, adam optimization, dropout, batch gradient descent, etc. without any clue how they work. Perhaps, like me, you find doing “batch norm in 1 line of code” to be unsatisfactory. What’s really going on?
And yes, although it was originally designed for Tensorflow 1 and Theano, everything has been done in Tensorflow 2 as well (you’ll see what I mean).
Cutting-Edge AI: Deep Reinforcement Learning in Python
Learn about awesome algorithms such as A2C, DDPG, and Evolution Strategies (ES). This course continues where my first Deep Reinforcement Learning course left off and is the third course in my Reinforcement Learning series.
Support Vector Machines
A lot of people think SVMs are obsolete. Wrong! A lot of you students want a nice “plug-and-play” model that works well out of the box. Guess what one of the best models is for that? SVM!
Many of the concepts from SVMs are extremely useful today – like quadratic programming (used for portfolio optimization) and constrained optimization.
Constrained optimization appears in modern Reinforcement Learning, for you non-believers (see: TRPO, PPO).
Well, I don’t need to tell you how popular GANs are. They sparked a mini-revolution in deep learning with the ability to generate photo-realistic images, create music, and enhance low-resolution photos.
Variational autoencoders are a great (but often forgotten by those beginner courses) tool for understanding and generating data (much like GANs) from a principled, probabilistic viewpoint.
Ever seen those cool illustrations where they can change a picture of a person from smiling to frowning on a continuum? That’s VAEs in action!
Supervised Machine Learning in Python
This is one of my favorite courses. Every beginner ML course these days teaches you how to plug into scikit-learn.
This is trivial. Everyone can do this. Nobody will give you a job just because you can write 3 lines of code when there are 1000s of others lining up beside you who know just as much.
It’s so trivial I teach it for FREE.
That’s why, in this course (a real ML course), I teach you how to not just use, but implement each of the algorithms (the fundamental supervised models).
At the same time, I haven’t forgotten about the “practical” aspect of ML, so I also teach you how to build a web API to serve your trained model.
This is the eventual place where many of your machine learning models will end up. What? Did you think you would just write a script that prints your accuracy and then call it a day? Who’s going to use your model?
The answer is, you’re probably going to serve it (over a server, duh) using a web server framework, such as Django, Flask, Tornado, etc.
Never written your own backend web server application before? I’ll show you how.
Alright, that’s all from me. Stay safe out there folks!
Note: these coupons will last 31 days – don’t wait!