# Don’t Forget! Deep Learning and Machine Learning Courses $10 Special! May 24, 2017 Just sending everyone a friendly reminder since this sale only has ONE DAY LEFT. This month, Udemy is having a special event called the “Udemy Learn Fest”, and you know I watch these things like a hawk so that when Udemy has their best deals I can bring the news to you as soon as they happen. As usual, I’m providing$10 coupons for all my courses in the links below. Please use these links and share them with your friends!

The $10 promo doesn’t come around often, so make sure you pick up everything you are interested in, or could become interested in later this year. The promo goes until May 25. Don’t wait! At the end of this post, I’m going to provide you with some additional links to get machine learning prerequisites (calculus, linear algebra, Python, etc…) for$10 too!

If you don’t know what order to take the courses in, please check here: https://deeplearningcourses.com/course_order

Here are the links for my courses:

Deep Learning Prerequisites: Linear Regression in Python
https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=MAY456

Deep Learning Prerequisites: Logistic Regression in Python
https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=MAY456

Deep Learning in Python
https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=MAY456

Practical Deep Learning in Theano and TensorFlow
https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=MAY456

Deep Learning: Convolutional Neural Networks in Python
https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=MAY456

Unsupervised Deep Learning in Python
https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=MAY456

Deep Learning: Recurrent Neural Networks in Python
https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=MAY456

Advanced Natural Language Processing: Deep Learning in Python
https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=MAY456

Advanced AI: Deep Reinforcement Learning in Python
https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=MAY456

Easy Natural Language Processing in Python
https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=MAY456

Cluster Analysis and Unsupervised Machine Learning in Python
https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=MAY456

Unsupervised Machine Learning: Hidden Markov Models in Python
https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=MAY456

Data Science: Supervised Machine Learning in Python
https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=MAY456

Bayesian Machine Learning in Python: A/B Testing
https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=MAY456

Ensemble Machine Learning in Python: Random Forest and AdaBoost

Artificial Intelligence: Reinforcement Learning in Python
https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=MAY456

SQL for Newbs and Marketers
https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=MAY456

And last but not least, $10 coupons for some helpful prerequisite courses. You NEED to know this stuff before you study machine learning: 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/2prFQ7o Probability (option 2) http://bit.ly/2p8kcC0 Probability (option 3) http://bit.ly/2oXa2pb Probability (option 4) http://bit.ly/2oXbZSK Remember, these links will self-destruct on May 25 (10 days). Act NOW! Go to comments # Deep Learning and Data Science courses on sale for$10!

April 24, 2017

Today, Udemy has decided to do yet another AMAZING $10 promo. As usual, I’m providing$10 coupons for all my courses in the links below. Please use these links and share them with your friends!

The $10 promo doesn’t come around often, so make sure you pick up everything you are interested in, or could become interested in later this year. The promo goes until April 29. Don’t wait! At the end of this post, I’m going to provide you with some additional links to get machine learning prerequisites (calculus, linear algebra, Python, etc…) for$10 too!

If you don’t know what order to take the courses in, please check here: https://deeplearningcourses.com/course_order

Here are the links for my courses:

Deep Learning Prerequisites: Linear Regression in Python
https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=APR456

Deep Learning Prerequisites: Logistic Regression in Python
https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=APR456

Deep Learning in Python
https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=APR456

Practical Deep Learning in Theano and TensorFlow
https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=APR456

Deep Learning: Convolutional Neural Networks in Python
https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=APR456

Unsupervised Deep Learning in Python
https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=APR456

Deep Learning: Recurrent Neural Networks in Python
https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=APR456

Advanced Natural Language Processing: Deep Learning in Python
https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=APR456

Advanced AI: Deep Reinforcement Learning in Python
https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=APR456

Easy Natural Language Processing in Python
https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=APR456

Cluster Analysis and Unsupervised Machine Learning in Python
https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=APR456

Unsupervised Machine Learning: Hidden Markov Models in Python
https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=APR456

Data Science: Supervised Machine Learning in Python
https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=APR456

Bayesian Machine Learning in Python: A/B Testing
https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=APR456

Ensemble Machine Learning in Python: Random Forest and AdaBoost

Artificial Intelligence: Reinforcement Learning in Python
https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=APR456

SQL for Newbs and Marketers
https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=APR456

And last but not least, $10 coupons for some helpful prerequisite courses. You NEED to know this stuff before you study machine learning: 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/2prFQ7o Probability (option 2) http://bit.ly/2p8kcC0 Probability (option 3) http://bit.ly/2oXa2pb Probability (option 4) http://bit.ly/2oXbZSK Remember, these links will self-destruct on April 29 (5 days). Act NOW! P.S. As you know, I’m ALWAYS updating my courses based on feedback and adding new material. Sometimes, even stuff that has been only recently invented! Here are a list of recent updates: – Deep Learning pt 1: Backpropagation troubleshooting (added to appendix). Use this if you have questions like “why do we sum over ‘k prime’?”, and “what is the chain rule?” – Recurrent Neural Networks (Deep Learning pt 5) and Deep NLP (Deep Learning pt 6): All language-modeling code can now train on the Brown corpus, which you can import directly from NLTK! No need to download and process Wikipedia data dumps anymore! This will make running the code much easier! – Deep Learning pt 2: Added code samples for grid search and random search, as well as a simple intuitive example of how dropout “emulates” an ensemble (which, by the way, you can gain even FURTHER insight into by taking my Ensemble Machine Learning course!) And coming very soon (next couple days): – Deep Learning pt 1: Using SKLearn so using a neural network is just 3 lines of code! – Recurrent Neural Networks (Deep Learning pt 5) and Deep NLP (Deep Learning pt 6): More discussion about why Tensorflow isn’t appropriate here, but at the same time, adding more Tensorflow examples! – Linear Regression and Logistic Regression: how to interpret the weights Go to comments # Udemy$10 coupons April 2017

April 6, 2017

Today, Udemy has decided to do yet another AMAZING $10 promo. As usual, I’m providing$10 coupons for all my courses in the links below. Please use these links and share them with your friends!

The $10 promo doesn’t come around often, so make sure you pick up everything you are interested in, or could become interested in later this year. The promo goes until April 12. Don’t wait! At the end of this post, I’m going to provide you with some additional links to get machine learning prerequisites (calculus, linear algebra, Python, etc…) for$10 too!

If you don’t know what order to take the courses in, please check here: https://deeplearningcourses.com/course_order

Here are the links for my courses:

Deep Learning Prerequisites: Linear Regression in Python
https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=APR123

Deep Learning Prerequisites: Logistic Regression in Python
https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=APR123

Deep Learning in Python
https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=APR123

Practical Deep Learning in Theano and TensorFlow
https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=APR123

Deep Learning: Convolutional Neural Networks in Python
https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=APR123

Unsupervised Deep Learning in Python
https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=APR123

Deep Learning: Recurrent Neural Networks in Python
https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=APR123

Advanced Natural Language Processing: Deep Learning in Python
https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=APR123

Advanced AI: Deep Reinforcement Learning in Python
https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=APR123

Easy Natural Language Processing in Python
https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=APR123

Cluster Analysis and Unsupervised Machine Learning in Python
https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=APR123

Unsupervised Machine Learning: Hidden Markov Models in Python
https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=APR123

Data Science: Supervised Machine Learning in Python
https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=APR123

Bayesian Machine Learning in Python: A/B Testing
https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=APR123

Ensemble Machine Learning in Python: Random Forest and AdaBoost

Artificial Intelligence: Reinforcement Learning in Python
https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=APR123

SQL for Newbs and Marketers
https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=APR123

And last but not least, $10 coupons for some helpful prerequisite courses. You NEED to know this stuff before you study machine learning: General (Site-wide coupon): http://bit.ly/2p3jHI8 Python http://bit.ly/2nMqqGg Calc 1 http://bit.ly/2oLayo8 Calc 2 http://bit.ly/2ocifGm Calc 3 http://bit.ly/2ocaNeo Linalg 1 http://bit.ly/2ocf4hO Linalg 2 http://bit.ly/2och8q2 Probability (option 1) http://bit.ly/2nZy4hy Probability (option 2) http://bit.ly/2nZN4vI Probability (option 3) http://bit.ly/2oLkY7A Probability (option 4) http://bit.ly/2o57IMG Remember, this post will self-destruct on April 12 (7 days). Act NOW! Go to comments # New Years Udemy Coupons! All Udemy Courses only$10

January 1, 2017

Act fast! These $10 Udemy Coupons expire in 10 days. Ensemble Machine Learning: Random Forest and AdaBoost https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=BOXINGDAY Deep Learning Prerequisites: Linear Regression in Python https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=BOXINGDAY Deep Learning Prerequisites: Logistic Regression in Python https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=BOXINGDAY Deep Learning in Python https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=BOXINGDAY Practical Deep Learning in Theano and TensorFlow https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=BOXINGDAY Deep Learning: Convolutional Neural Networks in Python https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=BOXINGDAY Unsupervised Deep Learning in Python https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=BOXINGDAY Deep Learning: Recurrent Neural Networks in Python https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=BOXINGDAY Advanced Natural Language Processing: Deep Learning in Python https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=BOXINGDAY Easy Natural Language Processing in Python https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=BOXINGDAY Cluster Analysis and Unsupervised Machine Learning in Python https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=BOXINGDAY Unsupervised Machine Learning: Hidden Markov Models in Python https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=BOXINGDAY Data Science: Supervised Machine Learning in Python https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=BOXINGDAY Bayesian Machine Learning in Python: A/B Testing https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=BOXINGDAY SQL for Newbs and Marketers https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=BOXINGDAY How to get ANY course on Udemy for$10 (please use my coupons above for my courses):

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# New course – Natural Language Processing: Deep Learning in Python part 6

August 9, 2016

[Scroll to the bottom for the early bird discount if you already know what this course is about]

In this course we are going to look at advanced NLP using deep learning.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.

These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.

First up is word2vec.

In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know.

Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:

• king – man = queen – woman
• France – Paris = England – London
• December – Novemeber = July – June

We are also going to look at the GLoVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.

Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train.

We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.

Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy and Matplotlib,and Theano. I am always available to answer your questions and help you along your data science journey.

See you in class!

https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=EARLYBIRDSITE

UPDATE: New coupon if the above is sold out:

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#deep learning #GLoVe #natural language processing #nlp #python #recursive neural networks #tensorflow #theano #word2vec

# Data Science: Natural Language Processing in Python

February 11, 2016

Do you want to learn natural language processing from the ground-up?

If you hate math and want to jump into purely practical coding examples, my new course is for you.

You can check it out at Udemy: https://www.udemy.com/data-science-natural-language-processing-in-python

I am posting the course summary here also for convenience:

In this course you will build MULTIPLE practical systems using natural language processing, or NLP. This course is not part of my deep learning series, so there are no mathematical prerequisites – just straight up coding in Python. All the materials for this course are FREE.

After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we’ll build is a spam detector. You likely get very little spam these days, compared to say, the early 2000s, because of systems like these.

Next we’ll build a model for sentiment analysis in Python. This is something that allows us to assign a score to a block of text that tells us how positive or negative it is. People have used sentiment analysis on Twitter to predict the stock market.

We’ll go over some practical tools and techniques like the NLTK (natural language toolkit) library and latent semantic analysis or LSA.

Finally, we end the course by building an article spinner. This is a very hard problem and even the most popular products out there these days don’t get it right. These lectures are designed to just get you started and to give you ideas for how you might improve on them yourself. Once mastered, you can use it as an SEO, or search engine optimization tool. Internet marketers everywhere will love you if you can do this for them!

As a thank you for visiting this site, I’ve created a coupon that gets you 70% off.

Click here to get the course for only \$15.

#article spinner #latent semantic analysis #latent semantic indexing #machine learning #natural language processing #nlp #pca #python #spam detection #svd

# Probability Smoothing for Natural Language Processing

January 23, 2016

Level: Beginner

Topic: Natural language processing (NLP)

This is a very basic technique that can be applied to most machine learning algorithms you will come across when you’re doing NLP.

Suppose for example, you are creating a “bag of words” model, and you have just collected data from a set of documents with a very small vocabulary. Your dictionary looks like this:

{"cat": 10, "dog": 10, "parrot": 10}

You would naturally assume that the probability of seeing the word “cat” is 1/3, and similarly P(dog) = 1/3 and P(parrot) = 1/3.

Now, suppose I want to determine the probability of P(mouse). Since “mouse” does not appear in my dictionary, its count is 0, therefore P(mouse) = 0.

This is a problem!

If you wanted to do something like calculate a likelihood, you’d have $$P(document) = P(words that are not mouse) \times P(mouse) = 0$$

This is where smoothing enters the picture.

We simply add 1 to the numerator and the vocabulary size (V = total number of distinct words) to the denominator of our probability estimate.

$$P(word) = \frac{word count + 1}{total number of words + V}$$

Now our probabilities will approach 0, but never actually reach 0.

For a word we haven’t seen before, the probability is simply:

$$P(new word) = \frac{1}{N + V}$$

You can see how this accounts for sample size as well.

If our sample size is small, we will have more smoothing, because N will be smaller.

## N-gram probability smoothing for natural language processing

An n-gram (ex. bigram, trigram) is a probability estimate of a word given past words.

For example, in recent years, $$P(scientist | data)$$ has probably overtaken $$P(analyst | data)$$.

In general we want to measure:

$$P(w_i | w_{i-1})$$

This probably looks familiar if you’ve ever studied Markov models.

You can see how such a model would be useful for, say, article spinning.

You could potentially automate writing content online by learning from a huge corpus of documents, and sampling from a Markov chain to create new documents.

Disclaimer: you will get garbage results, many have tried and failed, and Google already knows how to catch you doing it. It will take much more ingenuity to solve this problem.

The maximum likelihood estimate for the above conditional probability is:

$$P(w_i | w_{i-1}) = \frac{count(w_i | w_{i-1})}{count(w_{i-1})}$$

You can see that as we increase the complexity of our model, say, to trigrams instead of bigrams, we would need more data in order to estimate these probabilities accurately.

$$P(w_i | w_{i-1}, w_{i-2}) = \frac{count(w_i | w_{i-1}, w_{i-2})}{count(w_{i-1}, w_{i-2})}$$

So what do we do?

You could use the simple “add-1” method above (also called Laplace Smoothing), or you can use linear interpolation.

What does this mean? It means we simply make the probability a linear combination of the maximum likelihood estimates of itself and lower order probabilities.

It’s easier to see in math…

$$P(w_i | w_{i-1}, w_{i-2}) = \lambda_3 P_{ML}(w_i | w_{i-1}, w_{i-2}) + \lambda_2 P_{ML}(w_i | w_{i-1}) + \lambda_1 P_{ML}(w_i)$$

We treat the lambda’s like probabilities, so we have the constraints $$\lambda_i \geq 0$$ and $$\sum_i \lambda_i = 1$$.

The question now is, how do we learn the values of lambda?

One method is “held-out estimation” (same thing you’d do to choose hyperparameters for a neural network). You take a part of your training set, and choose values for lambda that maximize the objective (or minimize the error) of that training set.

If you have ever studied linear programming, you can see how it would be related to solving the above problem.

Another method might be to base it on the counts. This would work similarly to the “add-1” method described above. If we have a higher count for $$P_{ML}(w_i | w_{i-1}, w_{i-2})$$, we would want to use that instead of $$P_{ML}(w_i)$$. If we have a lower count we know we have to depend on$$P_{ML}(w_i)$$.

## Good-Turing smoothing and Kneser-Ney smoothing

These are more complicated topics that we won’t cover here, but may be covered in the future if the opportunity arises.

Have you had success with probability smoothing in NLP? Let me know in the comments below!