NEW COURSE: Financial Engineering and Artificial Intelligence in Python

September 8, 2020

Financial Engineering and Artificial Intelligence in Python

VIP Promotion

 

The complete Financial Engineering course has arrived

Hello once again friends!

Today, I am announcing the VIP version of my latest course: Financial Engineering and Artificial Intelligence in Python.

If you don’t want to read my little spiel just click here to get your VIP coupon:

https://www.udemy.com/course/ai-finance/?couponCode=FINANCEVIP (expires Oct 9, 2020)

https://www.udemy.com/course/ai-finance/?couponCode=FINANCEVIP2 (expires Nov 9, 2020)

(as usual, this coupon lasts only 30 days, so don’t wait!)

 

This is a MASSIVE (19 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

We will learn about the greatest flub made in the past decade by marketers posing as “machine learning experts” who promise to teach unsuspecting students how to “predict stock prices with LSTMs”. You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense. It is a lesson in how not to apply AI in finance.

 

List of VIP-only Contents

As with my Tensorflow 2 release, some of the VIP content will be a surprise and will be released in stages. Currently, the entirety of the Algorithmic Trading sections are VIP sections. Newly added VIP sections include Statistical Factor Models and “The Lazy Programmer Bonus Offer”. Here’s a full list:

 

Classic Algorithmic Trading – Trend Following Strategy

You will learn how moving averages can be applied to do algorithmic trading.

 

Machine Learning-Based Trading Strategy

Forecast returns in order to determine when to buy and sell.

 

Reinforcement Learning-Based (Q-Learning) Trading Strategy

I give you a full introduction to Reinforcement Learning from scratch, and then we apply it to build a Q-Learning trader. Note that this is *not* the same as the example I used in my Tensorflow 2, PyTorch, and Reinforcement Learning courses. I think the example included in this course is much more principled and robust.

 

Statistical Factor Models

The CAPM is one of the most renowned financial models in history, but did you know it’s only the simplest factor model, with just a single factor? To go beyond just this single factor model, we will learn about statistical factor models, where the multiple “factors” are found automatically using only the data.

The Lazy Programmer Bonus Offer

There are marketers out there who want to capitalize on your enthusiastic interest in finance, and unfortunately what they are teaching you is utter and complete garbage.

They will claim that they can “predict stock prices with LSTMs” and show you charts like this with nearly perfect stock price predictions.

Hint: if they can do this, why do they bother putting effort into making courses? Wouldn’t they already be billionaires?

Have you ever wondered if you are taking such a course from a fake data scientist / marketer? If so, just send me a message, and I will tell you whether or not you are taking such a course. (Hint: many of you are) I will give you a list of mistakes they made so you can look out for them yourself, and avoid “learning” things which will ultimately make YOU look very bad in front of potential future employers.

Believe me, if you ever try to get a job in machine learning or data science and you talk about a project where you “predicted stock prices with LSTMs”, all you will be demonstrating is how incompetent you are.

Save yourself from this embarrassing scenario by taking the “Lazy Programmer Offer”!

 

Please note: The VIP coupon will work only for the next month (ending Nov 9, 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 to these VIP contents on deeplearningcourses.com.

In case it’s not clear, the process is very easy. For those folks who need the “step-by-step” instructions…:

STEP 1) I announce the VIP content will be removed.

STEP 2) You email me with proof that you purchased the course during the VIP period. Do NOT email me earlier as it will just get buried.

STEP 3) I will give you free access to the VIP materials for this course on deeplearningcourses.com.

 

Benefits of taking this course

  • Learn the knowledge you need to work at top tier investment firms
  • Gain practical, real-world quantitative skills that can be applied within and outside of finance
  • Make better decisions regarding your own finances

 

Personally, I think this is the most interesting and action-packed course I have created yet. My last few courses were cool, but they were all about topics which I had already covered in the past! GANs, NLP, Transfer Learning, Recommender Systems, etc etc. all just machine learning topics I have covered several times in different libraries. This course contains new, fresh content and concepts I have never covered in any of my courses, ever.

This is the first course I’ve created that extends into a niche area of AI application. It goes outside of AI and into domain expertise. An in-depth topic such as finance deserves its own course. This is that course. These are topics you will never learn in a generic data science or machine learning course. However, as a student of AI, you will recognize many of our tools and methods being applied, such as statistical inference, supervised and unsupervised learning, convex optimization, and optimal control. This allows us to go deeper than your run of the mill financial engineering course, and it becomes more than just the sum of its parts.

So what are you waiting for?

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The complete PyTorch course for AI and Deep Learning has arrived

April 1, 2020

PyTorch: Deep Learning and Artificial Intelligence

VIP Promotion

The complete PyTorch course has arrived

Hello friends!

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)

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.

 

Minimal Prerequisites

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. =)

 

Why PyTorch?

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?

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Why bad programmers always need the latest version

October 26, 2020

Hello all!

Today, I’ve got 2 exciting things for you.

First, now is your chance to VOTE to tell me what you want to see in my next course. Transformers? Time Series Analysis? More advanced GANs? More advanced Reinforcement Learning? Let me know in this survey (it’s anonymous):

https://forms.gle/CNjjdaffSpi67qvq9

Second, check out the latest episode of The Lazy Programmer Show, where I discuss why bad programmers are always trying to get the latest version of some language or library, and why they tend to freak out when things are not in whatever version they happen to be using.

I look at this topic from 3 different perspectives, including:

1) What is it like in the “real world” working at a “real job”?

2) What kind of skills should a typical (competent) programmer have?

3) What are students learning and how do they approach machine learning and coding in colleges? Remember those students become “new grads” and those “new grads” become “junior engineers”.

What would your boss say if a junior engineer could run circles around you, a so-called “professional”?

Click here to watch the video https://www.youtube.com/watch?v=BIXH_m6CT2I or click the image below:

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Tensorflow 2 One Year Later: What do I think now? (+PyTorch, JAX, Julia)

September 24, 2020

In the latest episode of the Lazy Programmer Show, I give you my honest opinion of Tensorflow 2, one year later after creating the leading Tensorflow 2 course on Udemy.

Is it still good?

Is it still worth learning?

Why does stuff keep breaking / changing?

How does it compare to other offerings? (PyTorch, JAX, Julia)

Did you know JAX was created by Google? (thus, using Tensorflow doesn’t equate to “doing what Google’s doing”)

Julia is a totally different programming language popular with many data scientists and machine learning engineers. Will it replace Python?

PLUS, a little bonus (but you’ll have to watch the video to see what it is) 😉

Check out the video here: https://youtu.be/A9lvfm3k6m4

Or just watch it here:

 

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How to Build Your Own Computer Science Degree

August 29, 2020

Note: You can find the video lecture for this article at https://youtu.be/C-RZUWOBDpY

 

 

The following books can be used to study core computer science topics at the college / university level, to prepare yourself for machine learning, deep learning, artificial intelligence, and data science.

These are the books I recommend for building your own computer science degree. Remember! The goal is to do as many exercises as you can. It’s not to just watch 5 minute YouTube videos and then conclude “I understand everything! There’s no need for exercises!”

This quote from the video sums it up nicely: if you don’t find the problems, the problems will find you.

Common question: What about C++? Yes, C++ is excellent! Ideally, you will learn both C++ and Java, but obviously, these are not hard prerequisites for machine learning or data science.

 

 

 

Calculus: Early Transcendentals by James Stewart



Introduction to Linear Algebra by Gilbert Strang



Introduction to Probability by Bertsekas and Tsitsiklis



Big Java by Cay Horstmann



Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein



Disclaimer: this post contains Amazon affiliate links.

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New Exclusive Course: Linear Programming for Linear Regression in Python

July 14, 2020

If you’ve been to deeplearningcourses.com recently, you will have noticed that there is now a section for exclusive courses. These are courses that will *not* be on any other platforms, only deeplearningcourses.com.

These are what I’ve been calling “mini-courses” during their development and that’s what they are in spirit. They are:

  • Lower cost
  • Shorter in duration

There won’t be any time spent on stuff like appendices which most of you have already seen and are mainly for beginners.

The point of these courses is to have a faster turn-around time on course development. Sometimes, there are topics I want to cover really quickly that won’t ever become a full-sized course. They will also be used to cover more advanced topics.

Unfortunately, a lot of students on other platforms (e.g. Udemy) are complete beginners who have no desire advance and gain actual skill. They take “marketer-taught” courses which leads to a complex which I call “confidence without ability”. Dealing with such students is draining.

These mini-courses will bring us back to the old days (many of you have been around since then!) where the material was more concise, straight-to-the-point, and didn’t need “beginner reminders” all over the place.

Given that these mini-courses are much simpler for me to make, I expect there to be many more in the future.

This first exclusive mini-course is on Linear Programming for Linear Regression.

Many students in my Linear Regression course often ask, “What if I want to use absolute error instead of squared error?” This course answers exactly that question and more.

The solution is based on Linear Programming (LP).

We will also cover 2 other common problems: maximum absolute deviation and positive-only (or negative-only) error.

These kinds of problems are often found in professional fields such as quantitative finance, operations research, and engineering.

Each of these problems can be solved using Linear Programming with the Scipy library.

BONUS FACT: I have a new pen and tablet set up so most of the derivations in this course are done by hand – really truly old-school like the Linear/Logistic Regression days!

Get the course here: https://deeplearningcourses.com/c/linearprogramming-python

 

MATLAB for Students, Engineers, and Professionals in STEM

Another exclusive course which has already been on deeplearningcourses.com for some time is my original MATLAB course. This was the first course I ever made and is basically a collector’s item. The quality isn’t that great compared to what I am creating now, but obviously you will still learn a lot.

I’m including it in this newsletter to announce that I was able to dig up an extra section on probability that didn’t exist before. So the course now has 3 major sections:

  1. MATLAB basic operations and variables
  2. Signal processing with sound and images
  3. Probability and statistics

Get the course here: https://deeplearningcourses.com/c/matlab

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