Using Granger Causality to Determine Whether Twitter Sentiment Predicts Bitcoin Price Movement

February 22, 2022

In this article, we are again going to combine my current favorite subjects: natural language processing, time series analysis, and financial analysis.

Recently, I created a couple lectures covering Granger causality, so this topic is fresh on my mind.

In short, Granger causality is used to determine whether one time series can be used to forecast another (i.e. predict the future).

In these lectures, I demonstrated that some economics variables are Granger causal (in particular, GDP and term spread).

Of course, another easy application is to determine whether or not Twitter sentiment can predict cryptocurrency movements.

This post is based on this short publication: “Does Twitter Predict Bitcoin?” by Shen, D., Urquhart, A. and Wang, P. (2019) and can be found at https://centaur.reading.ac.uk/80420/1/Twitter.Bitcoin.pdf

The premise is quite simple and you really have to just understand these 3 components in order to implement this yourself:

1) How to get a Twitter sentiment time series

2) How to get Bitcoin price time series

3) How to implement the Granger causality test

If you can do 1-3, you can predict Bitcoin! (at least, partially)

So let’s go over each of these 3 topics in order.

 

How to get a Twitter sentiment time series

This is going to probably be the most difficult part for most students. Most students are used to downloading a CSV dataset that I typically make very nice and simple for my courses.

Unfortunately, real life is not like this.

This becomes a data engineering problem.

Which tweets by which authors do you choose?

How do you use Twitter’s API to download the tweets?

Where do you store the tweets?

Once you’ve figured that out, you need to convert the tweets into a number (sentiment) such that the numbers collectively form a time series.

That part is not so hard.

I’ve demonstrated several methods of doing this, such as:

a) training your own model on sentiment data (you could even create your own dataset)

b) using a pretrained Transformer model

 

How to get Bitcoin price time series

In contrast to the first task, this is probably the easiest.

In the past, I’ve demonstrated how you can easily get minute, daily, monthly, etc. data for essentially any ticker using the yfinance Python package.

 

How to implement the Granger causality test

For those of you who haven’t learned Time Series Analysis with me in the past, you perhaps have never heard of Granger causality.

In short, we build a multivariate autoregressive time series model called a VAR model.

It takes the form of:

$$y(t) = \sum_{\tau=1}^L A_\tau y(t-\tau) + \varepsilon(t)$$

Essentially, if you find any component \( A_\tau(j,i) \) is “big enough” (in magnitude), then you can conclude that \( y_i(t) \) Granger causes \( y_j(t) \).

As in regression analysis, one decides whether these model coefficients are statistically significant by using hypothesis testing.

It’s important to note that Granger causality is not “true” causality as one usually thinks of it (e.g. eating food causes me to be satiated). Granger causal simply means that one time series is useful in forecasting another (hence the cross-coefficients being non-zero).

Luckily, the Granger causality test is very easy to use in Python with the statsmodels package.

Suppose you have your 2 time series (BTC returns and Twitter sentiment) in a 2-column dataframe (sidenote: your time series should be stationary so you should use returns and not prices).

Then you simply call the statsmodels function:

This will output p-values for every lag so you can see whether or not the sentiment at that particular lag affects the BTC return.

Final note: unfortunately, the paper only shows that Twitter sentiment Granger causes some function of the squared return. This means we lose information about whether the return is actually going up or down!

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[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, the main part of the course (without the VIP parts) 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.

UPDATE 4: For those of you waiting for me to finish the rest of the course (e.g. deep learning sections) that has now been done. I’ve also added a big handful of advanced notebooks to the VIP content! (see “part 6” below)

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

Part 6: Even MORE bonus VIP notebooks (VIP only)

  • Stock Movement Prediction Using News
  • LSA / LSI for Recommendations
  • LSA / LSI for Classification (Feature Engineering)
  • LSA / LSI for Topic Modeling
  • LSA / LSI for Text Summarization (2 methods)
  • LSTM for Text Generation Notebook (i.e. the “decoder” part of an encoder-decoder network)
  • Masked language model with LSTM Notebook (revisiting the article spinner)

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. UPDATE: All post-release sections have been uploaded!

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/

 

Does this course replace “Natural Language Processing with Deep Learning in Python”, or “Deep Learning: Advanced NLP and RNNs”?

In fact, this course replaces neither of these more advanced NLP courses.

Let’s first consider “Natural Language Processing with Deep Learning in Python”.

This course covers more advanced topics, generally.

For instance, both variants of word2vec (skip-gram and CBOW) are discussed in detail and implemented from scratch. In the current course, only the very basic ideas are discussed.

Another word embedding algorithm called GloVe is taught in detail, along with a from-scratch implementation. In the current course, again it is only mentioned very briefly.

This course reviews RNNs, but goes into great detail on a completely new architecture, the “Recursive Neural Tensor Network”.

Essentially, this is a neural network structured like a tree, which is very useful for tasks such as sentiment analysis where negation of whole phrases may be desired (and easily accomplished with a tree structure).

 

How about “Deep Learning: Advanced NLP and RNNs”?

Again, there is essentially no overlap.

As the title suggests, this course covers more advanced topics. Like the previously mentioned course, it can be thought of as another sequel to the current course.

This course covers topics such as: bidirectional RNNs, seq2seq (for many-to-many tasks where the input length is not equal to the target length), attention (the central mechanism in transformers), and memory networks.

 

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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

GET THE COURSE HERE: https://deeplearningcourses.com/c/natural-language-processing-in-python

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SPECIAL SALE 90% OFF: Avoid public spaces; study Deep Learning

March 3, 2020

virusbanner

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

deep2

https://www.udemy.com/course/data-science-deep-learning-in-theano-tensorflow/?couponCode=MAR2020

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

rl3

https://www.udemy.com/course/cutting-edge-artificial-intelligence/?couponCode=MAR2020
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

svm

https://www.udemy.com/course/support-vector-machines-in-python/?couponCode=MAR2020

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).

 

GANs and Variational Autoencoders

gan

https://www.udemy.com/course/deep-learning-gans-and-variational-autoencoders/?couponCode=MAR2020

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

super

https://www.udemy.com/course/data-science-supervised-machine-learning-in-python/?couponCode=MAR2020

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!

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BLACK FRIDAY / CYBER MONDAY 2019 — Deep Learning and Artificial Intelligence in Python

November 28, 2019

Yearly Black Friday sale is HERE! As I always tell my students – you never know when Udemy’s next “sale drought” is going to be – so if you are on the fence about getting a course, NOW is the time.

NOTE: If you are looking for the Tensorflow 2.0 VIP materials, as of now they can only be purchased here: https://deeplearningcourses.com/c/deep-learning-tensorflow-2 (coupon code automatically applied). The site contains only the VIP materials, and the main part of the course can be purchased on Udemy as per the link below. Therefore, if you want the “full” version of the course, each part now must be purchased separately.

 

tf2

https://www.udemy.com/course/deep-learning-tensorflow-2/

  • What you’ll learn:
    • Neurons and Machine Learning
    • ANNs
    • CNNs
    • RNNs
    • GANs
    • NLP
    • Recommender Systems
    • Reinforcement Learning
    • build a stock trading bot with Deep RL
    • Low-level and advanced Tensorflow 2.0 features
    • Exporting models for Tensorflow Lite
    • Tensorflow Serving

rl3

https://www.udemy.com/course/cutting-edge-artificial-intelligence/

  • What you’ll learn: A2C, Evolution Strategies, and DDPG

svm

https://www.udemy.com/course/support-vector-machines-in-python/

  • What you’ll learn: 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

rec

https://www.udemy.com/course/recommender-systems/

  • What you’ll learn:
    • 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

nlp3

https://www.udemy.com/course/deep-learning-advanced-nlp/

  • What you’ll learn:
    • modern Deep NLP techniques such as Bidirectional LSTMs
    • CNNs for text classification
    • seq2seq
    • attention
    • memory networks

cv

https://www.udemy.com/course/advanced-computer-vision/

  • What you’ll learn:
    • 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

gan

https://www.udemy.com/course/deep-learning-gans-and-variational-autoencoders/

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

deeprl

https://www.udemy.com/course/deep-reinforcement-learning-in-python/

  • What you’ll learn:
    • 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

rl

https://www.udemy.com/course/artificial-intelligence-reinforcement-learning-in-python/

  • What you’ll learn:
    • Learn what makes Reinforcement Learning special compared to basic supervised/unsupervised learning (hint: it’s very complicated!)
    • Learn how epsilon-greedy and Bayesian machine learning can optimize click-through rates
    • Implement a tic-tac-toe agent
    • MDPs (Markov Decision Processes) and the Bellman equation
    • Learn the 3 approaches to RL: Dynamic Programming, Monte Carlo, and Temporal Difference (which includes the famous Q-Learning algorithm)

lin

https://www.udemy.com/course/data-science-linear-regression-in-python/

  • What you’ll learn:
    • 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

log

https://www.udemy.com/course/data-science-logistic-regression-in-python/

  • What you’ll learn:
    • 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)

deep1

https://www.udemy.com/course/data-science-deep-learning-in-python/

  • What you’ll learn:
    • 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

nlp

https://www.udemy.com/course/data-science-natural-language-processing-in-python/

  • What you’ll learn:
    • 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

deep2

https://www.udemy.com/course/data-science-deep-learning-in-theano-tensorflow/

  • What you’ll learn:
    • 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)

sql

https://www.udemy.com/course/sql-for-marketers-data-analytics-data-science-big-data/

  • What you’ll learn:
    • 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

cnn

https://www.udemy.com/course/deep-learning-convolutional-neural-networks-theano-tensorflow/

  • What you’ll learn:
    • 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

cluster

https://www.udemy.com/course/cluster-analysis-unsupervised-machine-learning-python/

  • What you’ll learn:
    • Learn about classic clustering methods such as K-Means, Hierarchical Clustering, and Gaussian Mixture Models (a probabilistic approach to Cluster Analysis)
    • Apply clustering to real-world datasets such as organizing books, clustering Hillary Clinton and Donald Trump tweets, and DNA

udeep

https://www.udemy.com/course/unsupervised-deep-learning-in-python/

  • What you’ll learn:
    • Learn about how Deep Learning an be applied to data without labels/targets using Autoencoders and RBMs (Restricted Boltzmann Machines)
    • Learn how Autoencoders are like a “nonlinear” version of PCA
    • Visualize / transform data with PCA and t-SNE
    • Apply RBMs to recommender systems

hmm

https://www.udemy.com/course/unsupervised-machine-learning-hidden-markov-models-in-python/

  • What you’ll learn:
    • Learn how unsupervised learning extends to cover sequences of data (like DNA, text processing, etc.)
    • The HMM is a probabilistic graphical model and uses the same learning approach (expectation-maximization) as k-means clustering and GMMs
    • We also review Markov models and you’ll see how they (surprisingly) apply to a famous modern algorithm: Google’s PageRank

rnn

https://www.udemy.com/course/deep-learning-recurrent-neural-networks-in-python/

  • What you’ll learn:
    • 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)

deepnlp

https://www.udemy.com/course/natural-language-processing-with-deep-learning-in-python/

  • What you’ll learn:
    • 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)

super

https://www.udemy.com/course/data-science-supervised-machine-learning-in-python/

  • What you’ll learn:
    • 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

bayes

https://www.udemy.com/course/bayesian-machine-learning-in-python-ab-testing/

  • What you’ll learn:
    • 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”

ensemble

https://www.udemy.com/course/machine-learning-in-python-random-forest-adaboost/

  • What you’ll learn:
    • 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

 

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Tensorflow 2.0 is here! Get the VIP version now

August 14, 2019

Tensorflow 2.0 is here!

***FINAL UPDATE***

Old coupon no longer works. Use this one instead: https://www.udemy.com/course/deep-learning-tensorflow-2/?couponCode=LASTVIP

PLEASE NOTE: VIP material will be removed from Udemy on November 27. If you signed up for the VIP version (using the VIP coupon) and want access beyond that point, you must email me at info [at] lazyprogrammer [dot] me.

If you want the VIP (full) version of the course beyond that date, you now need to purchase the “main” part and the “VIP” part separately. The “main” part can be purchased on Udemy and the “VIP” part can be purchased from: https://deeplearningcourses.com/c/deep-learning-tensorflow-2

 

—–

I am happy to announce my latest and most massive course yet – Tensorflow 2.0: Deep Learning and Artificial Intelligence.

Guys I am not joking – this really is my most massive course yet – check out the curriculum.

Many of you will be interested in the stock prediction example, because you’ve been tricked by marketers posing as data scientists in the past – I will demonstrate why their results are seriously flawed.

[if you don’t want to read my little spiel just click here to get your VIP coupon: https://www.udemy.com/deep-learning-tensorflow-2/?couponCode=TENSORVIP]

This is technically Deep Learning in Python part 12, but importantly this need not be the 12th deep learning course of mine that you take!

There are quite few important points to cover in this announcement, so let me outline what I will discuss:

A) What’s covered in this course
B) Why there are almost zero prerequisites for this course
C) The VIP content and near-term additions
D) The story behind this course (if you’ve been following my courses for some time you will be interested in this)

What’s covered in this course

As mentioned – this course is massive. It’s going to take you from basic linear models (the neuron) to ANNs, CNNs, and RNNs.

Thanks to the new standardized Tensorflow 2.0 API – we can move quickly.

The theme of this course is breadth, not depth. If you’re looking for heavy theory (e.g. backpropagation), well, I already have courses for those. So there’s no point in repeating that.

We will however go pretty in-depth to ensure that convolution (the main component of CNNs) and recurrent units (the main component of RNNs) are explained intuitively and from multiple perspectives.

These will include explanations and intuitions you have likely not seen before in my courses, so even if you’ve taken my CNN and RNN courses before, you will still want to see this.

There are many applications in this course. Here are a few:

– we will prove Moore’s Law using a neuron
– image classification with modern CNN design and data augmentation
– time series analysis and forecasting with RNNs

Anyone who is interested in stock prediction should check out the RNN section. Most RNN resources out there only look at NLP (natural language processing), including my old RNN course, but very few look at time series and forecasting.

And out of the ones that do, many do forecasting totally wrong!

There is one stock forecasting example I see everywhere, but its methodology is flawed. I will demonstrate why it’s flawed, and why stock prediction is not as simple as you have been led to believe.

There’s also a ton of Tensorflow-specific content, such as:

– Tensorflow serving (i.e. how to build a web service API from a Tensorflow model)
– Distributed training for faster training times (what Tensorflow calls “distribution strategies”)
– Low-level Tensorflow – this has changed completely from Tensorflow 1.x
– How to build your own models using the new Tensorflow 2.0 API
– Tensorflow Lite (how to export your models for mobile devices – iOS and Android) (coming soon)
– Tensorflow.js (how to export your models for the browser) (coming soon)

Why there are almost zero prerequisites for this course

Due to the new standardized Tensorflow 2.0 API, writing neural networks is easier than ever before.

This means that we’ll be able to blast through each section with very little theory (no backpropagation).

All you will need is a basic understanding of Python, Numpy, and Machine Learning, which are all taught in my free Numpy course.

As I always say, it’s free, so you have no excuses!

Tensorflow 2.0 however, does not invalidate or replace my other courses. If you haven’t taken them yet, you should take this course first for breadth, and then take the other courses which focus on individual models (CNNs, RNNs) for depth.

The VIP content and near-term additions

I had so much content in mind for this course, but I wanted to get this into your hands as soon as possible. With Tensorflow 2.0 due to be released any day now, I wanted to give you all a head start.

This field is moving so fast things were changing while I was making the course. Insane!

I’ll be adding more content in the coming weeks, possibly including but not limited to:

– Transfer Learning
– Natural Language Processing
– GANs
– Recommender Systems
– Reinforcement Learning

For this release, only the VIP version will be available for some time. That is why you do not see the usual Udemy discount.

You may be wondering: Which parts of the content are VIP content, and which are not?

This time, I wanted to do something interesting: it’s a surprise!

The VIP content will be added to a special section called the “VIP Section”, and this will be removed once the course becomes “Non-VIP”.

I will make an announcement well before that happens, so you will have the chance to download the VIP content before then, as well as get access to the VIP content permanently from deeplearningcourses.com.

The story behind this course

Originally, this course was going to be an RNN course only (hence why the RNN sections have so much more content – both time series and NLP).

The reason for this was, my original RNN course was tied to Theano and building RNNs from scratch.

In Tensorflow, building RNNs is completely different. This is unlike ANNs and CNNs which are relatively similar.

Thus, I could never reconcile the differences between the Theano approach and the Tensorflow approach in my original RNN course. So, I decided that simply making a new course for RNNs in Tensorflow would be best.

But lo and behold – Tensorflow was evolving so fast that a new version was about to be released – so I thought, it’s probably best to just cover everything in Tensorflow 2.0!

And that is how this current course came to be.

I hope you enjoy this action-packed course.

I’ll see you in class!

Get the course now
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[June 2019] AI / Machine Learning HUGE Summer Sale! $9.99

June 10, 2019

AI / Machine Learning Summer Sale

For the next week, all my Deep Learning and AI courses are available for just $9.99! (In addition to other courses on the site for the next few days)

For those of you who have been around for some time, you know that this sale doesn’t come around very often – just a few times per year. If you’ve been on the fence about getting a course, NOW is the time to do so. Get it now – save it for later.

For my courses, please use the coupons below (included in the links), or if you want, enter the coupon code: JUN2019.

As usual, if you want to know what order to take my courses in, check out the lecture “What order should I take your courses in?” in the Appendix of any of my courses (including the free Numpy course).

For prerequisite courses (math, stats, Python programming) and all other courses, follow the links at the bottom for sales of up to 90% off!

Since ALL courses on Udemy on sale, if you want any course not listed here, just click the general (site-wide) link, and search for courses from that page.


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https://www.udemy.com/support-vector-machines-in-python/?couponCode=JUN2019


https://www.udemy.com/recommender-systems/?couponCode=JUN2019


https://www.udemy.com/deep-learning-advanced-nlp/?couponCode=JUN2019


https://www.udemy.com/advanced-computer-vision/?couponCode=JUN2019


https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/?couponCode=JUN2019


https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=JUN2019


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PREREQUISITE COURSE COUPONS

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General (site-wide): http://bit.ly/2oCY14Z
Python http://bit.ly/2pbXxXz
Calc 1 http://bit.ly/2okPUib
Calc 2 http://bit.ly/2oXnhpX
Calc 3 http://bit.ly/2pVU0gQ
Linalg 1 http://bit.ly/2oBBir1
Linalg 2 http://bit.ly/2q5SGEE
Probability (option 1) http://bit.ly/2p8kcC0
Probability (option 2) http://bit.ly/2oXa2pb
Probability (option 3) http://bit.ly/2oXbZSK

 

OTHER UDEMY COURSE COUPONS

As you know, I’m the “Lazy Programmer”, not just the “Lazy Data Scientist” – I love all kinds of programming!

 

iOS courses:
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Android courses:
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Ruby on Rails courses:
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Python courses:
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Big Data (Spark + Hadoop) courses:
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Udemy St. Patrick’s Day Sale 🍀

March 13, 2019

Do beer and AI go together?

For the next week, all my Deep Learning and AI courses are available for just $11.99! ($1.00 less than the current sale, woohoo!)

For my courses, please use the coupons below (included in the links), or if you want, enter the coupon code: MAR2019.

For prerequisite courses (math, stats, Python programming) and all other courses, follow the links at the bottom for sales of up to 90% off!

Since ALL courses on Udemy on sale, if you want any course not listed here, just click the general (site-wide) link, and search for courses from that page.


https://www.udemy.com/support-vector-machines-in-python/?couponCode=MAR2019

https://www.udemy.com/recommender-systems/?couponCode=MAR2019
https://www.udemy.com/deep-learning-advanced-nlp/?couponCode=MAR2019


https://www.udemy.com/advanced-computer-vision/?couponCode=MAR2019


https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/?couponCode=MAR2019


https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=MAR2019


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PREREQUISITE COURSE COUPONS

And just as important, $11.99 coupons for some helpful prerequisite courses. You NEED to know this stuff to understand machine learning in-depth:

General (site-wide): http://bit.ly/2oCY14Z
Python http://bit.ly/2pbXxXz
Calc 1 http://bit.ly/2okPUib
Calc 2 http://bit.ly/2oXnhpX
Calc 3 http://bit.ly/2pVU0gQ
Linalg 1 http://bit.ly/2oBBir1
Linalg 2 http://bit.ly/2q5SGEE
Probability (option 1) http://bit.ly/2p8kcC0
Probability (option 2) http://bit.ly/2oXa2pb
Probability (option 3) http://bit.ly/2oXbZSK

 

OTHER UDEMY COURSE COUPONS

As you know, I’m the “Lazy Programmer”, not just the “Lazy Data Scientist” – I love all kinds of programming!

 

iOS courses:
https://lazyprogrammer.me/ios

Android courses:
https://lazyprogrammer.me/android

Ruby on Rails courses:
https://lazyprogrammer.me/ruby-on-rails

Python courses:
https://lazyprogrammer.me/python

Big Data (Spark + Hadoop) courses:
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Javascript, ReactJS, AngularJS courses:
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EVEN MORE COOL STUFF

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!

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How to Meet Your New Years Resolutions in 2019 (Udemy Coupons $9.99)

January 1, 2019

Deep Learning and AI Courses for just $9.99

New Years 2019

How to meet your New Years resolutions in 2019

Firstly, I’d like to wish everyone on this list a happy new year, we are off to a great start. The new year is a time to set goals, turn things around, and be better than we were before.

What better way than to learn from thousands of experts around the world who are the best at what they do? Luckily, I’ve got something that will make it just a little easier.

I know a lot of you have been waiting for this – well here it is – the LOWEST price possible on ALL Udemy courses (yes, the whole site!)

For the next 10 days, ALL courses on Udemy (not just mine) are available for just $9.99!

For my courses, please use the Udemy coupons below (included in the links below), or if you want, enter the coupon code: JAN2019.

For prerequisite courses (math, stats, Python programming) and all other courses (Bitcoin, meditation, yoga, guitar, photography, whatever else you want to learn), follow the links at the bottom (or go to my website).

Since ALL courses on Udemy are on sale, if you want any course not listed here, just click the general (site-wide) link, and search for courses from that page.


https://www.udemy.com/recommender-systems/?couponCode=JAN2019
https://www.udemy.com/deep-learning-advanced-nlp/?couponCode=JAN2019


https://www.udemy.com/advanced-computer-vision/?couponCode=JAN2019


https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/?couponCode=JAN2019


https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=JAN2019


https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=JAN2019


https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=JAN2019


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https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=JAN2019


https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=JAN2019


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https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=JAN2019


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https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=JAN2019


https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=JAN2019


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https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=JAN2019


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https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=JAN2019

 

PREREQUISITE COURSE COUPONS

And just as important, $9.99 coupons for some helpful prerequisite courses. You NEED to know this stuff to understand machine learning in-depth:

General (site-wide): http://bit.ly/2oCY14Z
Python http://bit.ly/2pbXxXz
Calc 1 http://bit.ly/2okPUib
Calc 2 http://bit.ly/2oXnhpX
Calc 3 http://bit.ly/2pVU0gQ
Linalg 1 http://bit.ly/2oBBir1
Linalg 2 http://bit.ly/2q5SGEE
Probability (option 1) http://bit.ly/2p8kcC0
Probability (option 2) http://bit.ly/2oXa2pb
Probability (option 3) http://bit.ly/2oXbZSK

 

OTHER UDEMY COURSE COUPONS

As you know, I’m the “Lazy Programmer”, not just the “Lazy Data Scientist” – I love all kinds of programming!

 

iOS courses:
https://lazyprogrammer.me/ios

Android courses:
https://lazyprogrammer.me/android

Ruby on Rails courses:
https://lazyprogrammer.me/ruby-on-rails

Python courses:
https://lazyprogrammer.me/python

Big Data (Spark + Hadoop) courses:
https://lazyprogrammer.me/big-data-hadoop-spark-sql

Javascript, ReactJS, AngularJS courses:
https://lazyprogrammer.me/javascript

 

EVEN MORE COOL STUFF

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!

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Artificial Intelligence Boxing Day Blowout!

December 26, 2018

Deep Learning and AI Courses for just $11.99

Boxing Day 2018

Celebrate the Holidays with New AI & Deep Learning Courses!

I’ve been busy making free content and updates for my existing courses, so guess what that means? Everything on sale!

For the next week, all my Deep Learning and AI courses are available for just $11.99!

For my courses, please use the coupons below (included in the links), or if you want, enter the coupon code: DEC2018.

For prerequisite courses (math, stats, Python programming) and all other courses, follow the links at the bottom for sales of up to 90% off!

Since ALL courses on Udemy on sale, if you want any course not listed here, just click the general (site-wide) link, and search for courses from that page.


https://www.udemy.com/recommender-systems/?couponCode=DEC2018


https://www.udemy.com/deep-learning-advanced-nlp/?couponCode=DEC2018


https://www.udemy.com/advanced-computer-vision/?couponCode=DEC2018


https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/?couponCode=DEC2018


https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=DEC2018


https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=DEC2018


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https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=DEC2018


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https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=DEC2018


https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=DEC2018

 

PREREQUISITE COURSE COUPONS

And just as important, $11.99 coupons for some helpful prerequisite courses. You NEED to know this stuff to understand machine learning in-depth:

General (site-wide): http://bit.ly/2oCY14Z
Python http://bit.ly/2pbXxXz
Calc 1 http://bit.ly/2okPUib
Calc 2 http://bit.ly/2oXnhpX
Calc 3 http://bit.ly/2pVU0gQ
Linalg 1 http://bit.ly/2oBBir1
Linalg 2 http://bit.ly/2q5SGEE
Probability (option 1) http://bit.ly/2p8kcC0
Probability (option 2) http://bit.ly/2oXa2pb
Probability (option 3) http://bit.ly/2oXbZSK

 

OTHER UDEMY COURSE COUPONS

As you know, I’m the “Lazy Programmer”, not just the “Lazy Data Scientist” – I love all kinds of programming!

 

iOS courses:
https://lazyprogrammer.me/ios

Android courses:
https://lazyprogrammer.me/android

Ruby on Rails courses:
https://lazyprogrammer.me/ruby-on-rails

Python courses:
https://lazyprogrammer.me/python

Big Data (Spark + Hadoop) courses:
https://lazyprogrammer.me/big-data-hadoop-spark-sql

Javascript, ReactJS, AngularJS courses:
https://lazyprogrammer.me/js

 

EVEN MORE COOL STUFF

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!

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