# 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 https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=APR456 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

# 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 https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=APR123 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!

# New course! Deep Reinforcement Learning in Python

March 27, 2017

Who’s ready for Deep Reinforcement Learning!!!???

Ever since I included this topic in my lecture called “Where does this course fit into my deep learning studies?”, people have been asking me about when my Deep Reinforcement Learning course is coming out.

Well, it’s out right now!

This course continues from where my last course, “Artificial Intelligence: Reinforcement Learning in Python”, left off.

In particular, we are going to be applying different kinds of neural networks to reinforcement learning, and also deepening our knowing of the RL algorithms we already learned about.

Of course, I’m going to link this with an early bird coupon. Get it now before they run out!

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

But… that’s not all.

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

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

Here are the links for my courses:

Deep Learning Prerequisites: Linear Regression in Python

https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=MAR456

Deep Learning Prerequisites: Logistic Regression in Python

https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=MAR456

Deep Learning in Python

https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=MAR456

Practical Deep Learning in Theano and TensorFlow

https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=MAR456

Deep Learning: Convolutional Neural Networks in Python

https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=MAR456

Unsupervised Deep Learning in Python

https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=MAR456

Deep Learning: Recurrent Neural Networks in Python

https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=MAR456

Advanced Natural Language Processing: Deep Learning in Python

https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=MAR456

Easy Natural Language Processing in Python

https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=MAR456

Cluster Analysis and Unsupervised Machine Learning in Python

https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=MAR456

Unsupervised Machine Learning: Hidden Markov Models in Python

https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=MAR456

Data Science: Supervised Machine Learning in Python

https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=MAR456

Bayesian Machine Learning in Python: A/B Testing

https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=MAR456

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=MAR456

SQL for Newbs and Marketers

https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=MAR456

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/2nYDImE Python http://bit.ly/2nYGMiP Calc 1 http://bit.ly/2nVPjT9 Calc 2 http://bit.ly/2mGwu6t Calc 3 http://bit.ly/2n7LsOg Linalg 1 http://bit.ly/2nlTNQn Linalg 2 http://bit.ly/2nr5Ugy Probability (option 1) http://bit.ly/2nDQENW Probability (option 2) http://bit.ly/2n8HENz Probability (option 3) http://bit.ly/2o7OfJd Probability (option 4) http://bit.ly/2nlYAkz P.S. I’ll be adding content to my Deep RL course in the coming days / weeks. Look for it to increase in length by 25-50%. Go to comments # Boston Dynamics – Introducing Handle February 28, 2017 Amazing! #artificial intelligence #boston dynamics #deep learning #reinforcement learning #robots Go to comments # New course! Reinforcement Learning in Python January 27, 2017 I would like to announce my latest course – Artificial Intelligence: Reinforcement Learning in Python. This has been one of my most requested topics since I started covering deep learning. This course has been brewing in the background for months. The result: This is my most MASSIVE course yet. Usually, my courses will introduce you to a handful of new algorithms (which is a lot for people to handle already). This course covers SEVENTEEN (17!) new algorithms. This will keep you busy for a LONG time. If you’re used to supervised and unsupervised machine learning, realize this: Reinforcement Learning is a whole new ball game. There are so many new concepts to learn, and so much depth. It’s COMPLETELY different from anything you’ve seen before. That’s why we build everything slowly, from the ground up. There’s tons of new theory, but as you’ve come to expect, anytime we introduce new theory it is accompanied by full code examples. What is Reinforcement Learning? It’s the technology behind self-driving cars, AlphaGo, video game-playing programs, and more. You’ll learn that while deep learning has been very useful for tasks like driving and playing Go, it’s in fact just a small part of the picture. Reinforcement Learning provides the framework that allows deep learning to be useful. Without reinforcement learning, all we have is a basic (albeit very accurate) labeling machine. With Reinforcement Learning, you have intelligence. Reinforcement Learning has even been used to model processes in psychology and neuroscience. It’s truly the closest thing we have to “machine intelligence” and “general AI”. What are you waiting for? Sign up now!! COUPON: https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=EARLYBIRDSITE #artificial intelligence #deep learning #reinforcement learning 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):

# 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.

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:

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

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

# New course – Deep Learning part 5: Recurrent Neural Networks in Python

July 14, 2016

New course out today – Recurrent Neural Networks in Python: Deep Learning part 5.

If you already know what the course is about (recurrent units, GRU, LSTM), grab your 50% OFF coupon and go!:

https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=WEBSITE

Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences – but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not – and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades.

Sequences appear everywhere – stock prices, language, credit scoring, and webpage visits.

Recurrent neural networks have a history of being very hard to train. It hasn’t been until recently that we’ve found ways around what is called the vanishing gradient problem, and since then, recurrent neural networks have become one of the most popular methods in deep learning.

If you took my course on Hidden Markov Models, we are going to go through a lot of the same examples in this class, except that our results are going to be a lot better.

Our classification accuracies will increase, and we’ll be able to create vectors of words, or word embeddings, that allow us to visualize how words are related on a graph.

We’ll see some pretty interesting results, like that our neural network seems to have learned that all religions and languages and numbers are related, and that cities and countries have hierarchical relationships.

If you’re interested in discovering how modern deep learning has propelled machine learning and data science to new heights, this course is for you.

I’ll see you in class.

https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=WEBSITE

#data science #deep learning #gru #lstm #machine learning #word vectors

# New course: Unsupervised Deep Learning in Python

May 15, 2016

This course is the next logical step in my deep learning, data science, and machine learning series. I’ve done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? Unsupervised deep learning!

In these course we’ll start with some very basic stuff – principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding).

Next, we’ll look at a special type of unsupervised neural network called the autoencoder. After describing how an autoencoder works, I’ll show you how you can link a bunch of them together to form a deep stack of autoencoders, that leads to better performance of a supervised deep neural network. Autoencoders are like a non-linear form of PCA.

Last, we’ll look at restricted Boltzmann machines (RBMs). These are yet another popular unsupervised neural network, that you can use in the same way as autoencoders to pretrain your supervised deep neural network. I’ll show you an interesting way of training restricted Boltzmann machines, known as Gibbs sampling, a special case of Markov Chain Monte Carlo, and I’ll demonstrate how even though this method is only a rough approximation, it still ends up reducing other cost functions, such as the one used for autoencoders. This method is also known as Contrastive Divergence or CD-k. As in physical systems, we define a concept called free energy and attempt to minimize this quantity.

Finally, we’ll bring all these concepts together and I’ll show you visually what happens when you use PCA and t-SNE on the features that the autoencoders and RBMs have learned, and we’ll see that even without labels the results suggest that a pattern has been found.

All the materials used in this course are FREE. Since this course is the 4th in the deep learning series, I will assume you already know calculus, linear algebra, and Python coding. You’ll want to install Numpy andTheano for this course. These are essential items in your data analytics toolbox.

If you are interested in deep learning and you want to learn about modern deep learning developments beyond just plain backpropagation, including using unsupervised neural networks to interpret what features can be automatically and hierarchically learned in a deep learning system, this course is for you.

Get your EARLY BIRD coupon for 50% off here: https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=EARLYBIRD

# New Deep Learning course on Udemy

February 26, 2016

This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow.

You learned about backpropagation (and because of that, this course contains basically NO MATH), but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.

You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn aboutadaptive learning rate techniques like AdaGrad and RMSprop which can also help speed up your training.

In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going to start from the basics so you understand exactly what’s going on – what are TensorFlow variables and expressions and how can you use these building blocks to create a neural network? We are also going to look at a library that’s been around much longer and is very popular for deep learning – Theano. With this library we will also examine the basic building blocks – variables, expressions, and functions – so that you can build neural networks in Theano with confidence.

Because one of the main advantages of TensorFlow and Theano is the ability to use the GPU to speed up training, I will show you how to set up a GPU-instance on AWS and compare the speed of CPU vs GPU for training a deep neural network.

With all this extra speed, we are going to look at a real dataset – the famous MNIST dataset (images of handwritten digits) and compare against various known benchmarks.