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

November 28, 2019

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

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

 

tf2

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

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

rl3

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

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

svm

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

  • What you’ll learn: Support Vector Machines (SVMs) in-depth starting from linear classification theory to the maximum margin method, kernel trick, quadratic programming, and the SMO (sequential minimal optimization) algorithm

rec

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

  • What you’ll learn:
    • Reddit and Hacker News algorithms
    • PageRank (what Google Search uses)
    • Bayesian / Thompson sampling
    • Collaborative filtering
    • Matrix factorization
    • We use the 20 million ratings dataset, not the puny 100k dataset everyone else uses
    • Implementing matrix factorization with Deep Learning
    • Using Deep Neural Networks for recommenders
    • Autoencoders for recommenders
    • Restricted Boltzmann Machines (RBMs) for recommenders
    • Recommenders with big data (PySpark) on AWS cluster

nlp3

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

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

cv

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

  • What you’ll learn:
    • Deep Learning techniques for computer vision, such as state-of-the-art networks (VGG, ResNet, Inception)
    • Train state-of-the-art models fast with transfer learning
    • Object detection with SSD
    • Neural style transfer

gan

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

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

deeprl

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

  • What you’ll learn:
    • Learn how we got from classical reinforcement learning to deep reinforcement learning and why it’s nontrivial
    • Play OpenAI Gym environments such as CartPole and Atari
    • Learn the “tricks” of DQN and A3C and how they improve classical RL approaches

rl

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

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

lin

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

  • What you’ll learn:
    • Learn about the most fundamental of machine learning algorithms: linear regression
    • Believe it or not, this gets you MOST of the way there to understanding deep learning

log

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

  • What you’ll learn:
    • After learning about linear regression, see how a similar model (logistic regression) can be used for classification
    • Importantly, understand how and why this is a model of the “neuron” (and because of that, we can use it to build neural networks)

deep1

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

  • What you’ll learn:
    • Learn IN-DEPTH the theory behind artificial neural networks (ANNs)
    • This is THE fundamental course for understanding what deep learning is doing, from ANNs to CNNs to RNNs to GANs and beyond

nlp

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

  • What you’ll learn:
    • Learn how to apply machine learning to NLP tasks, such as: spam detection, sentiment analysis, article spinning, and latent semantic analysis
    • Learn how to preprocess text for use in a ML algorithm
    • Learn about the classic NLTK library

deep2

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

  • What you’ll learn:
    • Learn how we went from the fundamental ANNs to many of the key technologies we use today, such as:
    • Batch / stochastic gradient descent instead of full gradient descent
    • (Nesterov) momentum, RMSprop, Adam, and other adaptive learning rate techniques
    • Dropout regularization
    • Batch normalization
    • Learn how deep learning is accelerated by GPUs (and how to set one up yourself)
    • Learn how deep learning libraries improve the development process with GPUs (faster training) and automatic differentiation (so you don’t have to write the code or derive the math yourself)

sql

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

  • What you’ll learn:
    • Learn the fundamentals of the SQL language and how to apply it to data
    • Practice for job interviews by going through several interview-style questions

cnn

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

  • What you’ll learn:
    • Go from ANNs to CNNs
    • Learn about the all important “convolution” operation in-depth
    • Implement convolution yourself (no other course does this!)
    • Design principles for CNNs and why they specialize to work with images

cluster

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

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

udeep

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

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

hmm

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

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

rnn

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

  • What you’ll learn:
    • Learn how Deep Learning handles sequences of data (like DNA, text processing, etc.)
    • Learn the limitations of a naive (simple) RNN
    • How to extend / improve RNNs with GRUs and LSTMs
    • Build GRUs and LSTMs by yourself (not just calling some library function)

deepnlp

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

  • What you’ll learn:
    • Apply deep learning to natural language processing (NLP)
    • Covers the famous word2vec and GloVe algorithms
    • See how RNNs apply to text problems
    • Learn about a neural network structured like a “tree” which we call recursive neural networks and a more powerful version: recursive neural tensor networks (RNTNs)

super

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

  • What you’ll learn:
    • Covers classic machine learning algorithms which EVERY student of machine learning should know (AND be able to implement)
    • K-Nearest Neighbor (KNN), Naive Bayes and non-Naive Bayes Classifiers, the Perceptron, and Decision Trees
    • Learn how to build a machine learning web service using Python server frameworks

bayes

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

  • What you’ll learn:
    • Learn how Bayesian machine learning differs from traditional machine learning
    • We focus mostly on “comparing” multiple things (i.e. A/B Testing)
    • Learn why traditional (frequentist) A/B Testing is limited
    • Learn about adaptive approaches to “choosing the best item”

ensemble

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

  • What you’ll learn:
    • Learn how combining multiple machine learning models is better than just one
    • Covers fundamental ensemble approaches such as Random Forest and AdaBoost
    • Learn/derive the famous “bias-variance tradeoff” (most people can only discuss it at a high level, you will learn what it really means)
    • Learn about the difference between the “bagging” and “boosting” approaches

 

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Tensorflow 2.0 is here! Get the VIP version now

August 14, 2019

Tensorflow 2.0 is here!

***FINAL UPDATE***

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

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

If you try to use the VIP coupon beyond that date (it will “technically” expire on Dec 21), it is your responsibility to make sure that (a) the VIP coupon did not get overridden (the price should be $49.99 USD), and (b) you ask me (via email) for access to the VIP contents along with proof-of-purchase.

 

—–

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

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

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

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

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

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

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

What’s covered in this course

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

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

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

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

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

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

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

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

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

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

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

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

Why there are almost zero prerequisites for this course

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

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

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

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

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

The VIP content and near-term additions

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

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

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

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

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

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

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

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

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

The story behind this course

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

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

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

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

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

And that is how this current course came to be.

I hope you enjoy this action-packed course.

I’ll see you in class!

Get the course now
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Retreat from the heat with Machine Learning and Artificial Intelligence

July 18, 2019

udemybannerjuly2019

For the next week, all my Deep Learning and AI courses are available for just $10.99!

Please use the coupons below (included in the links), or if you want, enter the coupon code: JUL2019.

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).


https://www.udemy.com/cutting-edge-artificial-intelligence/?couponCode=JUL2019


https://www.udemy.com/support-vector-machines-in-python/?couponCode=JUL2019


https://www.udemy.com/recommender-systems/?couponCode=JUL2019


https://www.udemy.com/deep-learning-advanced-nlp/?couponCode=JUL2019


https://www.udemy.com/advanced-computer-vision/?couponCode=JUL2019


https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/?couponCode=JUL2019


https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=JUL2019


https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=JUL2019


https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=JUL2019


https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=JUL2019


https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=JUL2019


https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=JUL2019


https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=JUL2019


https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=JUL2019


https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=JUL2019


https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=JUL2019


https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=JUL2019


https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=JUL2019


https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=JUL2019


https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=JUL2019


https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=JUL2019


https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=JUL2019


https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=JUL2019

PREREQUISITE COURSE COUPONS

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

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

OTHER UDEMY COURSE COUPONS

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

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

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

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

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

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

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

EVEN MORE COOL STUFF

Into Yoga in your spare time? Photography? Painting? There are courses, and I’ve got coupons! If you find a course on Udemy that you’d like a coupon for, just let me know and I’ll hook you up!

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MATLAB for Students, Engineers, and Professionals in STEM

June 25, 2019

Exciting news!

I’ve just RE-released my very first course (no longer available on any platform anywhere else), which was focused on MATLAB for signal processing with images and sound.

Crazy to think that I made this course FIVE years ago. This course was not even my idea!

It can be thought of as the MATLAB equivalent of my free Numpy course (which is for Python).

Of course, this is not for everybody, as MATLAB is not free and is a pretty niche language, but this should be nice for those of you who actually work with MATLAB either in school or at your job.

Or of course, you can get it just to support future content and to have a full collection. 😉

Click here to get MATLAB for Students, Engineers, and Professionals in STEM

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[June 2019] AI / Machine Learning HUGE Summer Sale! $9.99

June 10, 2019

AI / Machine Learning Summer Sale

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

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

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

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

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

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


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


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


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


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


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


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


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


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


https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=JUN2019


https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=JUN2019


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


https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=JUN2019


https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=JUN2019


https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=JUN2019


https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=JUN2019


https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=JUN2019


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


https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=JUN2019


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


https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=JUN2019


https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=JUN2019


https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=JUN2019


https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=JUN2019

 

PREREQUISITE COURSE COUPONS

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

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

 

OTHER UDEMY COURSE COUPONS

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

 

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

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

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

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

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

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

 

EVEN MORE COOL STUFF

Into Yoga in your spare time? Photography? Painting? There are courses, and I’ve got coupons! If you find a course on Udemy that you’d like a coupon for, just let me know and I’ll hook you up!

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