# 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 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 https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=MAR456 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%.

# Boston Dynamics – Introducing Handle

February 28, 2017

Amazing!

#artificial intelligence #boston dynamics #deep learning #reinforcement learning #robots

# Linear Regression

July 25, 2014

Code for this tutorial is here:

https://github.com/lazyprogrammer/machine_learning_examples/blob/master/linear_regression_class/lr_1d.py

Prerequisites for understanding this material:

• calculus (taking partial derivatives)

Linear regression is one of the simplest machine learning techniques you can use. It is often useful as a baseline relative to more powerful techniques.

To start, we will look at a simple 1-D case.

Like all regressions, we wish to map some input X to some input Y.

ie.

Y = f(X)

With linear regression:

Y = aX + b

Or we can say:

h(X) = aX + b

Where “h” is our “hypothesis”.

You may recall from your high school studies that this is just the equation for a straight line.

When X is 1-D, or when “Y has one explanatory variable”, we call this “simple linear regression”.

When we use linear regression, we are using it to model linear relationships, or what we think may be linear relationships.

As with all supervised machine learning problems, we are given labeled data points:

(X1, Y1), (X2, Y2), (X3, Y3), …, (Xn, Yn)

And we will try to fit the line (aX + b) as best we can to these data points.

This means we have to optimize the parameters “a” and “b”.

How do we do this?

We will define an error function and then find the “a” and “b” that will make the error as small as possible.

You will see that many regression problems work this way.

What is our error function?

We could use the difference between the predicted Y and the actual Y like so:

But if we had equal amounts of errors where Y was bigger than the prediction, and where Y was smaller than the prediction, then the errors would cancel out, even though the absolute difference in errors is large.

Typically in machine learning, the squared error is a good place to start.

Now, whether or not the difference in the actual and predicted output is positive or negative, its contribution to the total error is still positive.

We call this sum the “sum of squared errors”.

Recall that we want to minimize it.

Recall from calculus that to minimize something, you want to take its derivative.

Because there are two parameters, we have to take the derivatives both with respect to a and with respect to b, set them to 0, and solve for a and b.

Luckily, because the error function is a quadratic it increases as (a,b) get further and further away from the minimum.

As an exercise I will let you calculate the derivatives.

You will get 2 equations (the derivatives) and 2 unknowns (a, b). From high school math you should know how to solve this by rearranging the terms.

Note that these equations can be solved analytically. Meaning you can just plug and chug the values of your inputs and get the final value of a and b by blindly using a formula.

Note that this method is also called “ordinary least squares”.

Measuring the error (R-squared)

To determine how well our model fits the data, we need a measure called the “R-square”.

Note that in classification problems, we can simply use the “classification rate”, which is the number of correctly classified inputs divided by the total number of inputs. With the real-valued outputs we have in regression, this is not possible.

Here are the equations we use to predict the R-square.

SS(residual) is the sum of squared error between the actual and predicted output. This is the same as the error we were trying to minimize before!

SS(total) is the sum of squared error between each sample output and the mean of all the sample outputs, i.e. What the residual error would be if we just predicted the average output every time.

So the R-square then, is just how much better our model is compared to predicting the mean each time. If we just predicted the mean each time, the R-square would be 1-1=0. If our model is perfect, then the R-square would be 1-0=1.

Something to think about: If our model performs worse than predicting the mean each time, what would be the R-square value?

Limitations of Linear Regression

• It only models linear equations. You can model higher order polynomials (link to later post) but the model is still linear in its parameters.
• It is sensitive to outliers. Meaning if we have one data point very far away from all the others, it could “pull” the regression line in its direction, away from all the other data points, just to minimize the error.
#calculus #linear regression #machine learning #statistics