The Black Friday 2021 sale is on! I’m sending you links now which will give you the maximum possible discount during the Black Friday / Cyber Monday season (see below for specific dates).
For those students who are new (welcome!), you may not know that I have a whole catalog of machine learning and AI courses built up and continuously updated over the past 6 years, with separate in-depth courses covering nearly every topic in the field, including:
This is a MASSIVE (20 hours) Financial Engineering course covering the core fundamentals of financial engineering and financial analysis from scratch. We will go in-depth into all the classic topics, such as:
Exploratory data analysis, significance testing, correlations, alpha and beta
Time series analysis, simple moving average, exponentially-weighted moving average
Holt-Winters exponential smoothing model
ARIMA and SARIMA
Efficient Market Hypothesis
Random Walk Hypothesis
Time series forecasting (“stock price prediction”)
Modern portfolio theory
Efficient frontier / Markowitz bullet
Mean-variance optimization
Maximizing the Sharpe ratio
Convex optimization with Linear Programming and Quadratic Programming
Capital Asset Pricing Model (CAPM)
Algorithmic trading
In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as:
Regression models
Classification models
Unsupervised learning
Reinforcement learning and Q-learning
List of VIP content:
Classic Algorithmic Trading – Trend Following Strategy
Reinforcement Learning is the most general form of AI we know of so far – some speculate it is the way forward to mimic animal intelligence and attain “AGI” (artificial general intelligence).
This course covers:
The explore-exploit dilemma and the Bayesian bandit method
MDPs (Markov Decision Processes)
Dynamic Programming solution for MDPs
Monte Carlo Method
Temporal Difference Method (including Q-Learning)
Approximation Methods using RBF Neural Networks
Applying your code to OpenAI Gym with zero effort / code changes
Building a stock trading bot (different approach in each course!)
Tensorflow 2: Deep Learning and Artificial Intelligence VIP
Exclusive to deeplearningcourses.com only
===The complete Tensorflow 2 course has arrived===
Looking for the LOWEST PRICE POSSIBLE Udemy Coupons?
Please enjoy the below Black Friday coupons for the rest of my courses on Udemy.
The best part is, you don’t have to enter any coupon code at all. Simply clicking on the links below will automatically get you the best possible price.
*Note: a few of the courses below, marked with an asterisk (*) are not part of the Black Friday sale. However, if you purchase these courses at the current price, you will receive, upon request, complimentary access to the full VIP version of the course on deeplearningcourses.com. Just email me at [email protected] for free access with proof of purchase.
Support Vector Machines (SVMs) in-depth starting from linear classification theory to the maximum margin method, kernel trick, quadratic programming, and the SMO (sequential minimal optimization) algorithm
Learn how we went from the fundamental ANNs to many of the key technologies we use today, such as:
Batch / stochastic gradient descent instead of full gradient descent
(Nesterov) momentum, RMSprop, Adam, and other adaptive learning rate techniques
Dropout regularization
Batch normalization
Learn how deep learning is accelerated by GPUs (and how to set one up yourself)
Learn how deep learning libraries improve the development process with GPUs (faster training) and automatic differentiation (so you don’t have to write the code or derive the math yourself)
Apply deep learning to natural language processing (NLP)
Covers the famous word2vec and GloVe algorithms
See how RNNs apply to text problems
Learn about a neural network structured like a “tree” which we call recursive neural networks and a more powerful version: recursive neural tensor networks (RNTNs)
Learn how combining multiple machine learning models is better than just one
Covers fundamental ensemble approaches such as Random Forest and AdaBoost
Learn/derive the famous “bias-variance tradeoff” (most people can only discuss it at a high level, you will learn what it really means)
Learn about the difference between the “bagging” and “boosting” approaches
Remember, this is a very rare sale (only once per year!). If there’s anything you want or if you are on the fence and think you might be interested, get it NOW so that you don’t miss out!
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.
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
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!
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!
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!
A lot of you have been asking me… “When is the $10 sale coming back?”
And as you know, I share the news as soon as I find out – so here it is.
Black Friday is THE BIGGEST SALE OF THE YEAR.
This is the lowest price possible on Udemy.
I always make sure to mention to everyone: grab everything while you can because we just don’t know when the next big sale is going to be!
Don’t get stuck for months wondering… “When is the next $10 sale coming back?” Just get everything now! (Even if you don’t plan on taking the course for some time.)
Enough babbling, let’s get to the coupons. Remember: there’s no need to type in the coupon code manually – I’ve already provided the links so all you need to do is click and add to cart!
But just in case you’re curious – the coupon code is BLACKFRIDAY2017.
Also, make sure you scroll down to the bottom for some important updates.
In my last post, I relaunched my course “Modern Deep Learning in Python” which has more than doubled in size since its inception. Along with this re-release I offered a special VIP version of the course where you get a free 28-page tutorial on Tensorflow’s new Estimator API.
As mentioned, this deal is not going to last. In fact, 24 hours from now, it will be GONE FOREVER. Remember: you MUST use the VIP coupon to get the VIP material.
PREREQUISITE COURSE COUPONS
And just as important, $10 coupons for some helpful prerequisite courses. You NEED to know this stuff before you study machine learning:
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!
Remember, these links will self-destruct on November 28 (13 days). Act NOW!
Since I am still busy hacking away at my next course, we are going to do another HUGE sale. ALL courses on Udemy are now $10. Take this opportunity to grab as many courses as you can because you never know when the next sale is going to be!
As usual, I’m providing $10 coupons for all my courses in the links below. Please use these links and share them with your friends!
You can also just type in the coupon code “JUL456”.
The $10 promo doesn’t come around often, so make sure you pick up everything you are interested in, or could become interested in later this year. The promo goes until July 31. Don’t wait!
At the end of this post, I’m going to provide you with some additional links to get machine learning prerequisites (calculus, linear algebra, Python, etc…) for $10 too!
Today, Udemy has decided to do yet another AMAZING $10 promo.
As usual, I’m providing $10 coupons for all my courses in the links below. Please use these links and share them with your friends!
The $10 promo doesn’t come around often, so make sure you pick up everything you are interested in, or could become interested in later this year. The promo goes until April 12. Don’t wait!
At the end of this post, I’m going to provide you with some additional links to get machine learning prerequisites (calculus, linear algebra, Python, etc…) for $10 too!
Ever since I included this topic in my lecture called “Where does this course fit into my deep learning studies?”, people have been asking me about when my Deep Reinforcement Learning course is coming out.
Well, it’s out right now!
This course continues from where my last course, “Artificial Intelligence: Reinforcement Learning in Python”, left off.
In particular, we are going to be applying different kinds of neural networks to reinforcement learning, and also deepening our knowing of the RL algorithms we already learned about.
Of course, I’m going to link this with an early bird coupon. Get it now before they run out!
Today, Udemy has decided to do yet another AMAZING $10 promo.
As usual, I’m providing $10 coupons for all my courses in the links below. Please use these links and share them with your friends!
The $10 promo doesn’t come around often, so make sure you pick up everything you are interested in, or could become interested in later this year. The promo goes until the end of the month. Don’t wait!
At the end of this newsletter, I’m going to provide you with some additional links to get machine learning prerequisites (calculus, linear algebra, Python, etc…) for $10 too!
Here are the links for my courses:
Deep Learning Prerequisites: Linear Regression in Python
I would like to announce my latest course – Artificial Intelligence: Reinforcement Learning in Python.
This has been one of my most requested topics since I started covering deep learning. This course has been brewing in the background for months.
The result: This is my most MASSIVE course yet.
Usually, my courses will introduce you to a handful of new algorithms (which is a lot for people to handle already). This course covers SEVENTEEN (17!) new algorithms.
This will keep you busy for a LONG time.
If you’re used to supervised and unsupervised machine learning, realize this: Reinforcement Learning is a whole new ball game.
There are so many new concepts to learn, and so much depth. It’s COMPLETELY different from anything you’ve seen before.
That’s why we build everything slowly, from the ground up.
There’s tons of new theory, but as you’ve come to expect, anytime we introduce new theory it is accompanied by full code examples.
What is Reinforcement Learning? It’s the technology behind self-driving cars, AlphaGo, video game-playing programs, and more.
You’ll learn that while deep learning has been very useful for tasks like driving and playing Go, it’s in fact just a small part of the picture.
Reinforcement Learning provides the framework that allows deep learning to be useful.
Without reinforcement learning, all we have is a basic (albeit very accurate) labeling machine.
With Reinforcement Learning, you have intelligence.
Reinforcement Learning has even been used to model processes in psychology and neuroscience. It’s truly the closest thing we have to “machine intelligence” and “general AI”.
