# 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.

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

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

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

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

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

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

• 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

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”

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

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)

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

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)

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

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

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:
• 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)

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

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

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

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

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

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)

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)

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

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

• 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

# 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.

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!

# [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.

https://www.udemy.com/cutting-edge-artificial-intelligence/?couponCode=JUN2019

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/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! Go to comments # 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 ### 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:

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!

# 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 ### 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:

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!

# 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

# Black Friday 2018

### Udemy’s BIGGEST Sale of the YEAR is back!

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 7 days, ALL courses on Udemy (not just mine) are available for just $9.99! For my courses, please use the coupons below (included in the links below), or if you want, enter the coupon code: NOV2018. For prerequisite courses (math, stats, Python programming) and all other courses (yoga, guitar, photography, whatever else you want to learn), follow the links at the bottom. 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=NOV2018 ### 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:

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!

# NEW course! Recommender Systems and Deep Learning in Python

September 13, 2018

### Recommender Systems and Deep Learning in Python

So excited to tell you about my new course!

Believe it or not, almost all online businesses today make use of recommender systems in some way or another.

What do I mean by “recommender systems”, and why are they useful?

Let’s look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook.

Recommender systems form the very foundation of these technologies.

YouTube: Video dashboard (and recommendations to the right of every video)

This course is a big bag of tricks that make recommender systems work across multiple platforms.

We’ll look at popular news feed algorithms, like RedditHacker News, and Google PageRank.

We’ll look at Bayesian recommendation techniques that are being used by a large number of media companies today.

But this course isn’t just about news feeds.

Companies like AmazonNetflix, and Spotify have been using recommendations to suggest products, movies, and music to customers for many years now.

These algorithms have led to billions of dollars in added revenue.

So I assure you, what you’re about to learn in this course is very real, very applicable, and will have a huge impact on your business.

For those of you who like to dig deep into the theory to understand how things really work, you know this is my specialty and there will be no shortage of that in this course. We’ll be covering state of the art algorithms like matrix factorization and deep learning (making use of both supervised andunsupervised learning), and you’ll learn a bag full of tricks to improve upon baseline results.

Whether you sell products in your e-commerce store, or you simply write a blog – you can use these techniques to show the right recommendations to your users at the right time.

If you’re an employee at a company, you can use these techniques to impress your manager and get a raise!

I’ll see you in class!

GET THE COURSE NOW

Note: this course is NOT a part of my deep learning series (it’s not Deep Learning part 11) because while it contains a major deep learning component, a lot of the course uses non-deep learning techniques as well. The deep learning parts apply modified neural network architectures and deep learning technologies to the recommender problem.

# Windows: How to install Tensorflow, Theano, Keras, PyTorch, CNTK, and more on Windows

January 17, 2018

Historically, Windows users have had the most trouble installing deep learning, machine learning, data science, and AI libraries.

Just a few years ago this process would have taken you hours, if not days, to complete, if you completed it at all (as opposed to just giving up).

In this lecture, I walk you through how to install the following libraries on Windows:

• Numpy
• Scipy
• Matplotlib
• Pandas
• NLTK (for NLP)
• Scikit-Learn
• Tensorflow
• Theano
• Keras
• PyTorch
• CNTK
• OpenAI Gym
• OpenAI Gym Atari Extension
• ffmpeg (needed to save videos in OpenAI Gym)

# ALL Courses on Udemy $10 Again! December 19, 2017 ### Big Surprise! Earlier this week, I mentioned Udemy was doing a promotion on Tech courses only (if you were signed up for my newsletter you would have gotten the announcement). I’ve just heard news that they’ve opened up the$10 sale to ALL courses for the next 3 days only!

What this means: All my courses will continue to be on sale for $10 (just click the below links). But in addition, you can find other courses (including calculus and probability prerequisites) for$10 too!

With some time off, now is the PERFECT time to catch up on your deep learning / machine learning / data science skills. It’s almost 2018 and AI is rising faster than ever.

What better way than to grab all the deep learning courses you’ll ever want to take, for just $10? Don’t forget, this is the LOWEST possible price on Udemy – get these courses NOW. We really don’t know when the next big sale is going to be. If you want to type in the coupon code manually, it’s: WINTER2017 (remember, this is only for my courses). However, the coupon codes are included automatically in the links below. This sale lasts until Dec. 21 (3 days). Don’t wait! https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=WINTER2017 https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=WINTER2017 https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=WINTER2017 https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=WINTER2017 https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=WINTER2017 https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=WINTER2017 https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=WINTER2017 https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=WINTER2017 https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=WINTER2017 https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=WINTER2017 https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=WINTER2017 https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=WINTER2017 https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=WINTER2017 https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=WINTER2017 https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=WINTER2017 https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=WINTER2017 https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=WINTER2017 https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/?couponCode=WINTER2017 PREREQUISITE COURSE COUPONS And just as important,$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

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:

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!
Remember, these links will self-destruct on December 21 (3 days). Act NOW!