[NEW COURSE] Machine Learning: Natural Language Processing in Python (V2)

December 20, 2021

VIP Promotion

Machine Learning: Natural Language Processing in Python (V2)

===The Complete Natural Language Processing Course Has Arrived===

Hello friends!

Welcome to my latest course, on Natural Language Processing (NLP).

Don’t want to read my little spiel? Just click here to get the VIP discount (expires in 30 days – Jan 20, 2022!):

https://www.udemy.com/course/natural-language-processing-in-python/?couponCode=NLPVIP

UPDATE: The opportunity to get the VIP version on Udemy has expired. However, it is now available at a new low price. Click here to automatically get the current lowest price: https://bit.ly/3nT5fTX

UPDATE 2: Some of you may see the full price of $199 USD without any discount. This is because promotions going forward will now be decided by Udemy, so you will only get what they give. Such is the downside of not getting the VIP version. From what I hear, promotions happen quite often, so you should not have to wait too long.

UPDATE 3: I’ve updated the above with an actual coupon code, so ALL students should see a discount.

IMPORTANT INFO: For those of you who missed the VIP discount but still want access to the VIP content, scroll to the bottom of this post. For those who got the VIP version on Udemy and want to access the VIP content for free at its new permanent home, scroll to the bottom of this post.

 

“Wait a minute… don’t you already have like, 3 courses on NLP?”

Yes!

My first NLP course was released over 5 years ago. While there have been updates to it over the years, it has turned into a Frankenstein monster of sorts.

Therefore, the logical action was to simply start anew.

This course is another MASSIVE one – I say it’s basically 4 courses in 1 (not including the VIP section).

One of those “courses” (the ML part) is a revamp of my original 2016 NLP course. And therefore, this new course is actually a superset of NLP V1. The TL;DR: way more content, better organization.

Let’s get to the details:

Part 1: Vector models and text-preprocessing

  • Tokenization, stemming, lemmatization, stopwords, etc.
  • CountVectorizer and TF-IDF
  • Basic intro to word2vec and GloVe
  • Build a text classifier
  • Build a recommendation engine

Part 2: Probability models

  • Markov models and language models
  • Article spinner
  • Cipher decryption

Part 3: Machine learning

  • Spam detection with Naive Bayes
  • Sentiment analysis with Logistic Regression
  • Text summarization with TF-IDF and TextRank
  • Topic modeling with Latent Dirichlet Allocation and Non-negative Matrix Factorization
  • Latent semantic indexing (LSI / LSA) with PCA / SVD*
  • VIP only: Applying LSI to text summarization, topic modeling, classification, and recommendations*

Part 4: Deep learning*

  • Embeddings
  • Feedforward ANNs
  • CNNs
  • RNNs / LSTMs

Part 5: Beginner’s Corner on Transformers with Hugging Face (VIP only)

  • Sentiment analysis revisit
  • Text generation revisit
  • Article spinning revisit
  • Question-answering
  • Zero-shot classification

I’m sure many of you are most excited about the Transformers VIP section. Please note that this is not a full course on Transformers. As you know, I like to go very in-depth and as such, this is a topic which deserves its own course. This VIP section is a “beginner’s corner”-style set of lectures, which outlines the tasks that Transformers can do (listed above), along with code examples for each task. The Transformers “code” is very simple – basically just 1 or 2 lines. Don’t worry, the actual notebooks are much longer than that, and demonstrate real meaningful use-cases. The Transformer-specific part is just 1 or 2 lines – and that is great for practical purposes. It does not show you how to train or fine-tune a Transformer, only how to use existing models. If you just want to use Transformers and make use of these state-of-the-art models, but you don’t care about the nitty gritty details, this is perfect for you.

Is the VIP section only ideal for beginners? NO! Despite the name, this section will be useful for everyone, especially those who are interested in Transformers. This is quite a complex topic, and getting “good” with Transformers really requires a step-by-step approach. Think of this as the first step.

What is the “VIP version”? As usual, the VIP version of the course contains extra VIP content only available to those who purchase the course during the VIP period (i.e now). This content will be removed when it becomes a regular, non-VIP course, at which point I will make an announcement. All who sign up for the VIP version will retain access to the VIP content forever via my website, simply by letting me know via email you’d like access (you only need to email if I announce the VIP period is ending).

NOTE: If you are interested in Transformers, a lot of this course contains important prerequisites. The language models and article spinner from part 2 (“probability models”) are very important for understanding pre-training methods. The deep learning sections are very important for learning about embeddings and how neural networks deal with sequences.

NOTE: As per the last few releases, I’ve wanted to get the course into your hands as early as possible. Some sections are still in progress, specifically, those denoted with an asterisk (*) above.

New sections completed since original release (with more coming soon):

  • Text summarization
  • Topic modeling

So what are you waiting for? Get the VIP version of Natural Language Processing (V2) NOW:

 

 

For those who missed the VIP version but still want it:

Yes, you can still get the VIP contents! They can now be purchased separately on deeplearningcourses.com.

You can get it here: https://deeplearningcourses.com/c/natural-language-processing-in-python

 

For those of you who already purchased the VIP version and want to get setup with the VIP content on deeplearningcourses.com:

Email me with your name (exactly as it appears on Udemy) along with your date of purchase. I will look up your details to confirm.

If required, read more about how the “VIP version” works here: https://lazyprogrammer.me/how-do-vip-courses-work/

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NEW COURSE: Time Series Analysis, Forecasting, and Machine Learning in Python

June 16, 2021

Time Series Analysis, Forecasting, and Machine Learning in Python

VIP Promotion

The complete Time Series Analysis course has arrived

Hello friends!

2 years ago, I asked the students in my Tensorflow 2.0 course if they’d be interested in a course on time series. The answer was a resounding YES.

Don’t want to read the rest of this little spiel? Just get the coupon:

https://www.udemy.com/course/time-series-analysis/?couponCode=TIMEVIP

(Updated: Expires Feb 21, 2022) https://www.udemy.com/course/time-series-analysis/?couponCode=TIMEVIP8

(note: this VIP coupon expires in 30 days!)

Time series analysis is becoming an increasingly important analytical tool.

  • With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value.
  • COVID-19 has shown us how forecasting is an essential tool for driving public health decisions.
  • Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time.

Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.

We will cover techniques such as:

  • ETS and Exponential Smoothing
  • Holt’s Linear Trend Model
  • Holt-Winters Model
  • ARIMA, SARIMA, SARIMAX, and Auto ARIMA
  • ACF and PACF
  • Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)
  • Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests)
  • Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks)
  • GRUs and LSTMs for Time Series Forecasting

We will cover applications such as:

  • Time series forecasting of sales data
  • Time series forecasting of stock prices and stock returns
  • Time series classification of smartphone data to predict user behavior

The VIP version of the course (obtained by purchasing the course NOW during the VIP period) will cover even more exciting topics, such as:

  • AWS Forecast (Amazon’s state-of-the-art low-code forecasting API)
  • GARCH (financial volatility modeling)
  • FB Prophet (Facebook’s time series library)
  • And MORE (it’s a secret!)

As always, please note that the VIP period may not last forever, and if / when the course becomes “non-VIP”, the VIP contents will be removed. If you purchased the VIP version, you will retain permanent access to the VIP content via my website, simply by letting me know via email you’d like access (you only need to email if I announce the VIP period is ending).

Small note:

I wanted to get this course into your hands early. Some sections are still in the editing stages, particularly:

  • Convolutional Neural Networks (done, but more to be added later)
  • Recurrent Neural Networks (done, but more to be added later)
  • Vector Autoregression
  • (VIP) GARCH
  • (VIP) FB Prophet
  • +MORE VIP CONTENT (it’s a surprise!)

UPDATE: The crossed-out items have since been added. There is no timeline for the remaining “surprise” lectures – it’ll be a surprise! 😉

So what are you waiting for? Get the VIP version of Time Series Analysis NOW:

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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=FINANCEVIP17 (expires Feb 21, 2022)

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

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
  • Advanced Pandas Data Frame manipulation for time series and finance
  • 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.

Regime Detection with Hidden Markov Models (HMMs)

In the first section on financial basics, we learn how to model the distribution of returns. But can we really say “the” distribution, as if there is only one?

One important “stylized fact” about returns is that volatility “clusters” or “persists”. That is, large returns tend to be surrounded by more large returns, and small returns by more small returns.

In other words, returns are actually nonstationary and to build a more accurate model we should not assume that they all come from the same distribution at all times.

Using HMMs, we can model this behavior. HMMs allow you to model hidden state sequences (high volatility and low volatility regimes), from which observations (the actual returns) are generated.

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. I don’t want to see any of my students falling for this! Save yourself from this embarrassing scenario by taking the “Lazy Programmer Offer”!

Please note: The VIP coupon will work only for the next month (starting from the coupon creation time). 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 want 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]

https://www.udemy.com/course/pytorch-deep-learning/?couponCode=PYTORCHVIP22 (expires Feb 21, 2022)

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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[VIP COURSE UPDATE] Artificial Intelligence: Reinforcement Learning in Python

March 18, 2020

Artificial Intelligence: Reinforcement Learning in Python

VIP Promotion

Hello all!

In this post, I am announcing the VIP coupon to my course titled “Artificial Intelligence: Reinforcement Learning in Python”.

There are 2 places to get the course.

  1. Udemy, with this VIP coupon: https://www.udemy.com/course/artificial-intelligence-reinforcement-learning-in-python/?couponCode=REINFORCEVIP8 (expires Feb 21, 2022)
  2. Deep Learning Courses (coupon automatically applied): https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python

You may recognize this course as one that has already existed in my catalog – however, the course I am announcing today contains ALL-NEW material. The entire course has been gutted and every lecture contained within the course did not exist in the original version.

One of the most common questions I get from students in my PyTorch, Tensorflow 2, and Financial Engineering courses is: “How can I learn reinforcement learning?”

While I do cover RL in those courses, it’s very brief. I’ve essentially summarized 12 hours of material into 2. So by necessity, you will be missing some things.

While that serves as a good way to scratch the surface of RL, it doesn’t give you a true, in-depth understanding that you will get by actually learning each component of RL step-by-step, and most importantly, getting a chance to put everything into code!

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 Radial Basis Functions
  • Applying your code to OpenAI Gym with zero effort / code changes
  • Building a stock trading bot (different approach in each course!)

 

When you get the DeepLearningCourses.com version, note that you will get both versions (new and old) of the course – totalling nearly 20 hours of material.

If you want access to the tic-tac-toe project, this is the version you should get.

Otherwise, if you prefer to use Udemy, that’s fine too. If you purchase on Udemy but would like access to DeepLearningCourses.com, I will allow this since they are the same price. Just send me an email and show me your proof of purchase.

Note that I’m not able to offer the reverse (can’t give you access to Udemy if you purchase on DeepLerningCourses.com, due to operational reasons).

So what are you waiting for?

 

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FREE Exercise: Predict Stocks with News, + Other ML News

January 19, 2022

TL;DR: this is an article about how to predict stocks using the news.

In this article, we are going to do an exercise involving my 2 current favorite subjects: natural language processing and financial engineering!

I’ll present this as an exercise / tutorial, so hopefully you can follow along on your own.

One comment I frequently make about predicting stocks is that autoregressive time series models aren’t really a great idea.

Basic analysis (e.g. ACF, PACF) shows no serial correlation in returns (that is, there’s no correlation between past and future) and hence, the future is not predictable from the past.

The best-fitting ARIMA model is more often than not, a simple random walk.

What is a random walk? If you haven’t yet learned this from me, then basically think of it like flipping a coin at each time step. The result of the coin flip tells you which way to walk: up the street or down the street.

Just as you can’t predict the result of a coin flip from past coin flips (by the way, this is essentially the gambler’s fallacy!), so too is it impossible to predict the next step of a random walk.

In these situations, the best prediction is simply the last-known value.

This is why, when one tries to fit an LSTM to a stock price time series, all it ends up doing is predicting close to the previous value.

There is a nice quote which is unfortunately (as far as I know) unattributed, that says something like: “trying to predict the future from the past is like trying to drive by looking through the rearview mirror”.

Anyway, this brings us to the question: “If I don’t use past prices, then what do I use?”

One common approach is to use the news.

We’ve all seen that news and notable events can have an impact on stock / cryptocurrency prices. Examples:

  • The Omicron variant of COVID-19
  • High inflation
  • Supply-chain issues
  • Elon Musk tweeting about Dogecoin
  • Mark Zuckerberg being grilled by the government

Luckily, I’m not going to make you scrape the web to download news yourself.

Instead, we’re going to use a pre-built dataset, which you can get at: https://www.kaggle.com/aaron7sun/stocknews

Briefly, you’ll want to look at the “combined” CSV file which has the following columns:

  • Date (e.g. 2008-08-11 – daily data)
  • Label (0 or 1 – whether or not the DJIA went up or down)
  • Top1, Top2, …, Top25 (news in the form of text, retrieved from the top 25 Reddit news posts)

Note that this is a binary classification problem.

Thanks to my famous rule, “all data is the same“, your code should be no different than a simple sentiment analysis / spam detection script.

To start you off, I’ll present some basic starter code / tips.

 

Tip 1) Some text contains weird formatting, e.g.

b”Georgia ‘downs two Russian warplanes’ as cou…

Basically, it looks like how a binary string would be printed out, but the “b” is part of the actual string.

Here’s a simple way to remove unwanted characters:

 

Tip 2) Don’t forget that this is time-ordered data, so you don’t want to do a train-test split with shuffling (mixing future and past in the train and test sets). The train set should only contain data that comes before the test set.

 

Tip 3) A simple way to form feature vectors from the news would be to just concatenate all 25 news columns into a single text, and then apply TF-IDF. E.g.

I’ll leave the concatenation part as an exercise for you.

 

Here are some extra thoughts to consider:

  • How were the labels created? Does that method make sense? Is it based on close-close or open-close?
  • What were the exact times that the news was posted? Was there sufficient time between the latest news post and the result from which the label is computed?
  • Returns tend to be very noisy. If you’re getting something like 85% test accuracy, you should be very suspicious that you’ve done something wrong. A more realistic result would be around 50-60%. Even 60% would be considered suspiciously high.

 

So that’s basically the exercise. It is simple, yet hopefully thought-provoking.

 

Now I didn’t know where else to put this ML news I found recently, but I enjoyed it so I want to share it with you all.

First up: “Chatbots: Still Dumb After All These Years

I enjoyed this article because I get a lot of requests to cover Chatbots.

Unfortunately, Chatbot technology isn’t very good.

Previously, we used seq2seq (and also seq2seq with attention) which basically just learns to copy canned responses to various inputs. seq2seq means “sequence to sequence” so the input is a sequence (a prompt) and the target/output is a sequence (the chatbot’s response).

Even with Transformers, the best results are still lacking.

 

Next: “PyTorch vs TensorFlow in 2022

Wait, people are still talking about this in 2022? You betcha!

Read this article. It says a lot of the same stuff I’ve been saying myself. But it’s nice to hear it from someone else.

It also provides actual metrics which I am too lazy to do.

 

Finally: “Facebook’s advice to students interested in artificial intelligence

This isn’t really “new news” (in fact, Facebook isn’t even called Facebook anymore) but I recently came across this old article I saved many years earlier.

Probably the most common beginner question I get is “why do I need to do all this math?” (in my ML courses).

You’ve heard the arguments from me hundreds of times.

Perhaps you are hesitant to listen to me. That would be like listening to your parents. Yuck.

Instead, why not listen to Yann LeCun? Remember that guy? The guy who invented CNNs?

He’s the Chief AI Scientist at Facebook (Meta) now, so if you want a job there, you should probably listen to his advice…

And if you think Google, Netflix, Amazon, Microsoft, etc. are any different, well, that is wishful thinking my friends.

What do you think?

Is this convincing? Or is Yann LeCun just as wrong as I am?

Let me know!

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List of Hugging Face Pipelines for NLP

December 11, 2021

Here is a list of Hugging Face Pipelines for NLP. For some reason these are difficult to find on Hugging Face’s own documentation, so I am listing them here for my own convenience (and yours).

  • sentiment-analysis
  • feature-extraction (convert text into a vector)
  • ner (named entity recognition)
  • text-generation
  • fill-mask (“article spinning”)
  • summarization
  • translation (e.g. translation_en_to_fr)
  • question-answering
  • zero-shot-classification
  • conversational (“chat bot”)
  • text2text-generation

Non-text pipelines:

  • image-classification
  • image-segmentation
  • object-detection
  • audio-classification

TODO: Link to course here. Let me know (using the “contact” form above) if you read this and I haven’t yet updated this link to my course.

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🎄 BLACK FRIDAY 2021 COUPONS AND DEALS

November 26, 2021

Hello friends!

 

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:

– Machine Learning (Linear Regression, Logistic Regression, K-Means Clustering, GMMs, Hierarchical Clustering, HMMs, Markov Models, Decision Trees, Random Forests, Naive Bayes, Perceptrons, SVMs, boosting, Bayesian ML, A/B Testing)

– NLP (Attention, seq2seq, BiLSTMs, word2vec, GloVe, article spinning, latent semantic indexing, sentiment analysis, spam detection)

– Reinforcement Learning (the basics, along with modern Deep RL topics like DQN, Policy Gradient Methods, Evolution Strategies, A2C and A3C, DDPG)

– Deep Learning (separate courses for ANNs, CNNs, RNNs, GANs, Variational Autoencoders, Recommender Systems, Computer Vision)

BOOKMARK THIS POST because these links will give you the best discount possible over the Black Friday / Cyber Monday sales later this month.

The “Black Friday” links will work on the following dates:

– Friday November 26, 2021 (Black Friday)

– All week before Black Friday (starting November 19)

– Monday November 29, 2021 (Cyber Monday)

– Maybe a few days after that, but don’t wait and regret it

 

 

Outline of the following discounts:

1. Time Series Analysis, Forecasting, and Machine Learning (VIP)

2. Financial Engineering and Artificial Intelligence in Python (VIP)

3. PyTorch: Deep Learning and Artificial Intelligence (VIP)

4. Artificial Intelligence: Reinforcement Learning in Python (VIP)

5. Black Friday Discounts for Other Courses

Note that the “VIP links” will work for the next 30 days, you don’t need to wait until Black Friday for those.

Time Series Analysis, Forecasting, and Machine Learning (VIP PROMOTION)

==The Complete Time Series Analysis Course Has Arrived==

https://www.udemy.com/course/time-series-analysis/?couponCode=TIMEVIP6

(note: this VIP coupon expires in 30 days!)

We will cover techniques such as:

  • ETS and Exponential Smoothing
  • Holt’s Linear Trend Model
  • Holt-Winters Model
  • ARIMA, SARIMA, SARIMAX, and Auto ARIMA
  • ACF and PACF
  • Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)
  • Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests)
  • Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks)
  • GRUs and LSTMs for Time Series Forecasting

We will cover applications such as:

  • Time series forecasting of sales data
  • Time series forecasting of stock prices and stock returns
  • Time series classification of smartphone data to predict user behavior

The VIP version (obtained by purchasing the course NOW during the VIP period) of the course covers even more exciting topics, such as:

  • AWS Forecast (Amazon’s state-of-the-art low-code forecasting API)
  • GARCH (financial volatility modeling)
  • FB Prophet (Facebook’s time series library)

 

Financial Engineering and Artificial Intelligence in Python

VIP Promotion

==The complete Financial Engineering course has arrived==

https://www.udemy.com/course/ai-finance/?couponCode=FINANCEVIP15

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
  • Machine Learning-Based Trading Strategy
  • Reinforcement Learning-Based (Q-Learning) Trading Strategy
  • Statistical Factor Models
  • Regime Detection and Volatility Clustering with HMMs and Sequence Modeling

 

PyTorch: Deep Learning and Artificial Intelligence

VIP Promotion

=====The complete PyTorch course has arrived=====

https://www.udemy.com/course/pytorch-deep-learning/?couponCode=PYTORCHVIP20

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
  • VIP only: Making predictions with your trained NLP model
  • VIP only: Making predictions with your trained Recommender model
  • VIP only: Modeling prediction uncertainty and heteroskedasticity (i.e. stock returns!)
  • VIP only: Facial recognition with Siamese Networks

 

Artificial Intelligence: Reinforcement Learning in Python

VIP Promotion

===The complete Reinforcement Learning course has arrived===

https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python

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===

Get it here: https://deeplearningcourses.com/c/deep-learning-tensorflow-2

 

BLACK FRIDAY DISCOUNTED Courses

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.

www.udemy.com/course/deep-learning-tensorflow-2/?referralCode=E10B72D3848AB70FE1B8

  • Machine Learning and Neurons
  • ANNs, CNNs, RNNs
  • NLP (Natural Language Processing)
  • GANs
  • Recommender Systems
  • Deep Reinforcement Learning (build a “stock trading bot”)
  • Transfer Learning for Computer Vision

 

www.udemy.com/course/cutting-edge-artificial-intelligence/?referralCode=12A3B9950D525ECB4557

  • Deep Reinforcement Learning algorithms such as A2C, Evolution Strategies, and DDPG


https://www.udemy.com/course/support-vector-machines-in-python/?referralCode=8EDBF7E0BD5AF7C1545D (*)

  • 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

www.udemy.com/course/recommender-systems/?referralCode=E33FEBAEF42C85B8FA8F

  • Reddit and Hacker News algorithms
  • PageRank (what Google Search uses)
  • Bayesian / Thompson sampling
  • Collaborative filtering
  • Matrix factorization
  • We use the 20 million ratings dataset, not the puny 100k dataset everyone else uses
  • Implementing matrix factorization with Deep Learning
  • Using Deep Neural Networks for recommenders
  • Autoencoders for recommenders
  • Restricted Boltzmann Machines (RBMs) for recommenders
  • Recommenders with big data (PySpark) on AWS cluster

www.udemy.com/course/deep-learning-advanced-nlp/?referralCode=A9F4F0A8E6479BE90D55

  • Modern Deep NLP techniques such as Bidirectional LSTMs, CNNs for text classification, seq2seq, attention, and memory networks

www.udemy.com/course/advanced-computer-vision/?referralCode=E75E8CDDEBB5A91A666F

  • Deep Learning techniques for computer vision, such as state-of-the-art networks (VGG, ResNet, Inception)
  • Train state-of-the-art models fast with transfer learning
  • Object detection with SSD
  • Neural style transfer


www.udemy.com/course/deep-learning-gans-and-variational-autoencoders/?referralCode=A7980E2D769910C847F9 (*)

  • Generate realistic, high quality images with deep neural networks
  • Apply game theory and Bayesian machine learning to deep learning
  • Learn about the “transpose convolution”


www.udemy.com/course/deep-reinforcement-learning-in-python/?referralCode=1FE6DB1ECC128417A7F1 (*)

  • Learn how we got from classical reinforcement learning to deep reinforcement learning and why it’s nontrivial
  • Play OpenAI Gym environments such as CartPole and Atari
  • Learn the “tricks” of DQN and A3C and how they improve classical RL approaches

www.udemy.com/course/data-science-linear-regression-in-python/?referralCode=A6A896C0AC0F14D7872D

  • Learn about the most fundamental of machine learning algorithms: linear regression
  • Believe it or not, this gets you MOST of the way there to understanding deep learning

 

www.udemy.com/course/data-science-logistic-regression-in-python/?referralCode=273355008B7AA8360E36

  • After learning about linear regression, see how a similar model (logistic regression) can be used for classification
  • Importantly, understand how and why this is a model of the “neuron” (and because of that, we can use it to build neural networks)

www.udemy.com/course/data-science-deep-learning-in-python/?referralCode=4B846F3BB454BE9DDB7F

  • Learn IN-DEPTH the theory behind artificial neural networks (ANNs)
  • This is THE fundamental course for understanding what deep learning is doing, from ANNs to CNNs to RNNs to GANs and beyond

www.udemy.com/course/data-science-natural-language-processing-in-python/?referralCode=1605639AC0DEFC0A44CB

  • Learn how to apply machine learning to NLP tasks, such as: spam detection, sentiment analysis, article spinning, and latent semantic analysis
  • Learn how to preprocess text for use in a ML algorithm
  • Learn about the classic NLTK library

www.udemy.com/course/data-science-deep-learning-in-theano-tensorflow/?referralCode=1AF84E4E63D850617F0E

  • 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)

 

www.udemy.com/course/sql-for-marketers-data-analytics-data-science-big-data/?referralCode=F664E44E42037CB0491F

  • Learn the fundamentals of the SQL language and how to apply it to data
  • Practice for job interviews by going through several interview-style questions

 

www.udemy.com/course/deep-learning-convolutional-neural-networks-theano-tensorflow/?referralCode=09CFE3EECB330B7F1FCC

  • Go from ANNs to CNNs
  • Learn about the all important “convolution” operation in-depth
  • Implement convolution yourself (no other course does this!)
  • Design principles for CNNs and why they specialize to work with images

 

 

www.udemy.com/course/deep-learning-recurrent-neural-networks-in-python/?referralCode=C4A5301EF4FAE5255D51

  • Learn how Deep Learning handles sequences of data (like DNA, text processing, etc.)
  • Learn the limitations of a naive (simple) RNN
  • How to extend / improve RNNs with GRUs and LSTMs
  • Build GRUs and LSTMs by yourself (not just calling some library function)

 


www.udemy.com/course/natural-language-processing-with-deep-learning-in-python/?referralCode=78A60E6BD16F3A656EA7 (*)

  • 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)

 


www.udemy.com/course/data-science-supervised-machine-learning-in-python/?referralCode=14513C7EEDFDF1EBD49F (*)

  • Covers classic machine learning algorithms which EVERY student of machine learning should know (AND be able to implement)
  • K-Nearest Neighbor (KNN), Naive Bayes and non-Naive Bayes Classifiers, the Perceptron, and Decision Trees
  • Learn how to build a machine learning web service using Python server frameworks

 

www.udemy.com/course/bayesian-machine-learning-in-python-ab-testing/?referralCode=8312098927EDB63AF429

  • Learn how Bayesian machine learning differs from traditional machine learning
  • We focus mostly on “comparing” multiple things (i.e. A/B Testing)
  • Learn why traditional (frequentist) A/B Testing is limited
  • Learn about adaptive approaches to “choosing the best item”

 


www.udemy.com/course/machine-learning-in-python-random-forest-adaboost/?referralCode=0210246BE75FD01DDF5F (*)

  • 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!

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Mistakes in Stock Prediction: Trying to Predict the Price

November 15, 2021

Hello friends! Curious about how to properly predict stock prices?

 

I’ve now released the third video in this YouTube mini-series.

For those unfamiliar: this is a video series that debunks common mistakes found in nearly all blog articles / Github repos claiming to do “stock price predictions with LSTMs”.

These are typically written by non-experts in the field just looking for clicks, and I have a lot of fun breaking down precisely what they’re doing wrong.

Why is this important?

Beginners are often fooled by such content, wasting money on courses to learn things that don’t work. Even worse, they may end up putting such examples on their own Github accounts or in their portfolios / resumes, worsening their chances of getting a job in the field.

To be clear: the bad part isn’t that they learned something that doesn’t work (although that is pretty bad by itself). The bad part is, they don’t even understand why it doesn’t work. They are confident that it does and will fight me over it! (That is, until I ask them to verify or rebut any of the claims I’ve made).

Thus, not only does this stuff not work (due to all the mistakes I outline in my video series), but it could actually be detrimental to getting a job and working in this area. These are not just harmless mistakes!

 

The first video discussed why min-max scaling over the train set doesn’t work.

The second video discussed why using prices as inputs (i.e. lagged prices to build an autoregressive model) doesn’t work.

This third video (this video) will discuss why using prices as targets does not work.

Enjoy!

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