New Exclusive Course: Linear Programming for Linear Regression in Python

July 14, 2020

If you’ve been to deeplearningcourses.com recently, you will have noticed that there is now a section for exclusive courses. These are courses that will *not* be on any other platforms, only deeplearningcourses.com.

These are what I’ve been calling “mini-courses” during their development and that’s what they are in spirit. They are:

  • Lower cost
  • Shorter in duration

There won’t be any time spent on stuff like appendices which most of you have already seen and are mainly for beginners.

The point of these courses is to have a faster turn-around time on course development. Sometimes, there are topics I want to cover really quickly that won’t ever become a full-sized course. They will also be used to cover more advanced topics.

Unfortunately, a lot of students on other platforms (e.g. Udemy) are complete beginners who have no desire advance and gain actual skill. They take “marketer-taught” courses which leads to a complex which I call “confidence without ability”. Dealing with such students is draining.

These mini-courses will bring us back to the old days (many of you have been around since then!) where the material was more concise, straight-to-the-point, and didn’t need “beginner reminders” all over the place.

Given that these mini-courses are much simpler for me to make, I expect there to be many more in the future.

This first exclusive mini-course is on Linear Programming for Linear Regression.

Many students in my Linear Regression course often ask, “What if I want to use absolute error instead of squared error?” This course answers exactly that question and more.

The solution is based on Linear Programming (LP).

We will also cover 2 other common problems: maximum absolute deviation and positive-only (or negative-only) error.

These kinds of problems are often found in professional fields such as quantitative finance, operations research, and engineering.

Each of these problems can be solved using Linear Programming with the Scipy library.

BONUS FACT: I have a new pen and tablet set up so most of the derivations in this course are done by hand – really truly old-school like the Linear/Logistic Regression days!

Get the course here: https://deeplearningcourses.com/c/linearprogramming-python

 

MATLAB for Students, Engineers, and Professionals in STEM

Another exclusive course which has already been on deeplearningcourses.com for some time is my original MATLAB course. This was the first course I ever made and is basically a collector’s item. The quality isn’t that great compared to what I am creating now, but obviously you will still learn a lot.

I’m including it in this newsletter to announce that I was able to dig up an extra section on probability that didn’t exist before. So the course now has 3 major sections:

  1. MATLAB basic operations and variables
  2. Signal processing with sound and images
  3. Probability and statistics

Get the course here: https://deeplearningcourses.com/c/matlab

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The complete PyTorch course for AI and Deep Learning has arrived

April 1, 2020

PyTorch: Deep Learning and Artificial Intelligence

VIP Promotion

The complete PyTorch course has arrived

Hello friends!

I hope you are all staying safe. Well, I’m sure you’ve heard enough about that so how about some different news?

Today, I am announcing the VIP version of my latest course: PyTorch: Deep Learning and Artificial Intelligence

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

https://www.udemy.com/course/pytorch-deep-learning/?couponCode=PYTORCHVIP16 (expires Aug 17, 2021)

This is a MASSIVE (over 22 hours) Deep Learning course covering EVERYTHING from scratch. That includes:

  • Machine learning basics (linear neurons)
  • ANNs, CNNs, and RNNs for images and sequence data
  • Time series forecasting and stock predictions (+ why all those fake data scientists are doing it wrong)
  • NLP (natural language processing)
  • Recommender systems
  • Transfer learning for computer vision
  • GANs (generative adversarial networks)
  • Deep reinforcement learning and applying it by building a stock trading bot

IN ADDITION, you will get some unique and never-before-seen VIP projects:

Estimating prediction uncertainty

Drawing the standard deviation of the prediction along with the prediction itself. This is useful for heteroskedastic data (that means the variance changes as a function of the input). The most popular application where heteroskedasticity appears is stock prices and stock returns – which I know a lot of you are interested in.

It allows you to draw your model predictions like this:


Sometimes, the data is simply such that a spot-on prediction can’t be made. But we can do better by letting the model tell us how certain it is in its predictions.

Facial recognition with siamese networks

This one is cool. I mean, I don’t have to tell you how big facial recognition has become, right? It’s the single most controversial technology to come out of deep learning. In the past, we looked at simple ways of doing this with classification, but in this section I will teach you about an architecture built specifically for facial recognition.

You will learn how this can work even on small datasets – so you can build a network that recognizes your friends or can even identify all of your coworkers!

You can really impress your boss with this one. Surprise them one day with an app that calls out your coworkers by name every time they walk by your desk. 😉

Please note: The VIP coupon will work only for the next month (ending May 1, 2020). It’s unknown whether the VIP period will renew after that time.

After that, although the VIP content will be removed from Udemy, all who purchased the VIP course will get permanent free access on deeplearningcourses.com.

Minimal Prerequisites

This course is designed to be a beginner to advanced course. All that is required is that you take my free Numpy prerequisites to learn some basic scientific programming in Python. And it’s free, so why wouldn’t you!?

You will learn things that took me years to learn on my own. For many people, that is worth tens of thousands of dollars by itself.

There is no heavy math, no backpropagation, etc. Why? Because I already have courses on those things. So there’s no need to repeat them here, and PyTorch doesn’t use them. So you can relax and have fun. =)

Why PyTorch?

All of my deep learning courses until now have been in Tensorflow (and prior to that Theano).

So why learn PyTorch?

Does this mean my future deep learning courses will use PyTorch?

In fact, if you have traveled in machine learning circles recently, you will have noticed that there has been a strong shift to PyTorch.

Case in point: OpenAI switched to PyTorch earlier this year (2020).

Major AI shops such as Apple, JPMorgan Chase, and Qualcomm have adopted PyTorch.

PyTorch is primarily maintained by Facebook (Facebook AI Research to be specific) – the “other” Internet giant who, alongside Google, have a strong vested interest in developing state-of-the-art AI.

But why PyTorch for you and me? (aside from the fact that you might want to work for one of the above companies)

As you know, Tensorflow has adopted the super simple Keras API. This makes common things easy, but it makes uncommon things hard.

With PyTorch, common things take a tiny bit of extra effort, but the upside is that uncommon things are still very easy.

Creating your own custom models and inventing your own ideas is seamless. We will see many examples of that in this course.

For this reason, it is very possible that future deep learning courses will use PyTorch, especially for those advanced topics that many of you have been asking for.

Because of the ease at which you can do advanced things, PyTorch is the main library used by deep learning researchers around the world. If that’s your goal, then PyTorch is for you.

In terms of growth rate, PyTorch dominates Tensorflow. PyTorch now outnumbers Tensorflow by 2:1 and even 3:1 at major machine learning conferences. Researchers hold that PyTorch is superior to Tensorflow in terms of the simplicity of its API, and even speed / performance!

Do you need more convincing?

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SPECIAL SALE 90% OFF: Avoid public spaces; study Deep Learning

March 3, 2020

virusbanner

Hello deep learning and AI enthusiasts!

As we all know, the near future is somewhat uncertain. With an invisible virus spreading around the world at an alarming rate, some experts have suggested that it may reach a significant portion of the population.

Schools may close, you may be ordered to work from home, or you may want to avoid going outside altogether. This is not fiction – it’s already happening.

There will be little warning, and as students of science and technology, we should know how rapidly things can change when we have exponential growth (just look at AI itself).

Have you decided how you will spend your time?

I find moments of quiet self-isolation to be excellent for learning advanced or difficult concepts – particularly those in machine learning and artificial intelligence.

To that end, I’ll be releasing several coupons today – hopefully that helps you out and you’re able to study along with me.

Modern Deep Learning in Python

deep2

https://www.udemy.com/course/data-science-deep-learning-in-theano-tensorflow/?couponCode=MAR2020

Despite the fact that I just released a huge course on Tensorflow 2, this course is more relevant than ever. You might take a course that uses batch norm, adam optimization, dropout, batch gradient descent, etc. without any clue how they work. Perhaps, like me, you find doing “batch norm in 1 line of code” to be unsatisfactory. What’s really going on?

And yes, although it was originally designed for Tensorflow 1 and Theano, everything has been done in Tensorflow 2 as well (you’ll see what I mean).

Cutting-Edge AI: Deep Reinforcement Learning in Python

rl3

https://www.udemy.com/course/cutting-edge-artificial-intelligence/?couponCode=MAR2020
Learn about awesome algorithms such as A2C, DDPG, and Evolution Strategies (ES). This course continues where my first Deep Reinforcement Learning course left off and is the third course in my Reinforcement Learning series.

Support Vector Machines

svm

https://www.udemy.com/course/support-vector-machines-in-python/?couponCode=MAR2020

A lot of people think SVMs are obsolete. Wrong! A lot of you students want a nice “plug-and-play” model that works well out of the box. Guess what one of the best models is for that? SVM!

Many of the concepts from SVMs are extremely useful today – like quadratic programming (used for portfolio optimization) and constrained optimization.

Constrained optimization appears in modern Reinforcement Learning, for you non-believers (see: TRPO, PPO).

 

GANs and Variational Autoencoders

gan

https://www.udemy.com/course/deep-learning-gans-and-variational-autoencoders/?couponCode=MAR2020

Well, I don’t need to tell you how popular GANs are. They sparked a mini-revolution in deep learning with the ability to generate photo-realistic images, create music, and enhance low-resolution photos.

Variational autoencoders are a great (but often forgotten by those beginner courses) tool for understanding and generating data (much like GANs) from a principled, probabilistic viewpoint.

Ever seen those cool illustrations where they can change a picture of a person from smiling to frowning on a continuum? That’s VAEs in action!

 

Supervised Machine Learning in Python

super

https://www.udemy.com/course/data-science-supervised-machine-learning-in-python/?couponCode=MAR2020

This is one of my favorite courses. Every beginner ML course these days teaches you how to plug into scikit-learn.

This is trivial. Everyone can do this. Nobody will give you a job just because you can write 3 lines of code when there are 1000s of others lining up beside you who know just as much.

It’s so trivial I teach it for FREE.

That’s why, in this course (a real ML course), I teach you how to not just use, but implement each of the algorithms (the fundamental supervised models).

At the same time, I haven’t forgotten about the “practical” aspect of ML, so I also teach you how to build a web API to serve your trained model.

This is the eventual place where many of your machine learning models will end up. What? Did you think you would just write a script that prints your accuracy and then call it a day? Who’s going to use your model?

The answer is, you’re probably going to serve it (over a server, duh) using a web server framework, such as Django, Flask, Tornado, etc.

Never written your own backend web server application before? I’ll show you how.
Alright, that’s all from me. Stay safe out there folks!

Note: these coupons will last 31 days – don’t wait!

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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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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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Black Friday 2018 – Udemy’s BIGGEST Sale of the YEAR is back!

November 14, 2018

Deep Learning and AI Courses for just $9.99

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


https://www.udemy.com/deep-learning-advanced-nlp/?couponCode=NOV2018


https://www.udemy.com/advanced-computer-vision/?couponCode=NOV2018


https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/?couponCode=NOV2018


https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=NOV2018


https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=NOV2018


https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=NOV2018


https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=NOV2018


https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=NOV2018


https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=NOV2018


https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=NOV2018


https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=NOV2018


https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=NOV2018


https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=NOV2018


https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=NOV2018


https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=NOV2018


https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=NOV2018


https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=NOV2018


https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=NOV2018


https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=NOV2018


https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?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:
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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Deep Learning $10 Udemy coupons + LAST DAY for VIP bonus

August 21, 2017

It’s that time again!

BIG DISCOUNTS for everyone! If you’re in the USA you should see $10 coupons. If you’re in another country you’ll see the corresponding amount in your own currency.

But before we get to that, I want to mention that the VIP bonus for my latest Deep Learning course on GANs and Variational Autoencoders is CLOSING TODAY.

So if you want to get the VIP bonus and you haven’t gotten it yet, NOW is the time!

Just a reminder of what you get:

1) PDF cheatsheet / tutorial on Variational Autoencoders for your reading convenience

2) PDF cheatsheet / tutorial on GANs for your reading convenience (with exercises)

3) Pre-trained style transfer network! No need to train for 4 months on your slow CPU, or pay hundreds of dollars to use a GPU, or download 100s of MBs of Tensorflow checkpoint data! I’ve condensed the neural network weights to a few MBs so you can get going right away.

If you don’t know what “style transfer” is – that’s where I train a neural network to learn the “style” of Picasso or Da Vinci, and then apply it to a completely unrelated image like the Chicago skyline.

Very cool application of neural networks!

Remember: these VIP bonuses are ONLY available if you use the VIP coupon, which is automatically applied when you click this link:

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

Now, for the regular $10 discounts (check the end of this newsletter for how to get $10 coupons for ANY course on Udemy this week!):

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Deep Learning Prerequisites: Logistic Regression in Python https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=AUG456​

Deep Learning in Python https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=AUG456​

Practical Deep Learning in Theano and TensorFlow https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=AUG456​

Deep Learning: Convolutional Neural Networks in Python https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=AUG456​

Unsupervised Deep Learning in Python https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=AUG456​

Deep Learning: Recurrent Neural Networks in Python https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=AUG456​

Advanced Natural Language Processing: Deep Learning in Python https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=AUG456​

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Bayesian Machine Learning in Python: A/B Testing https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=AUG456​

Ensemble Machine Learning in Python: Random Forest and AdaBoost https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=AUG456​

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EVEN MORE COOL STUFF

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Remember, these links will self-destruct on August 31 (10 days). Act NOW!

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New course! Reinforcement Learning in Python

January 27, 2017

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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:

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#artificial intelligence #deep learning #reinforcement learning

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Announcing Data Science: Supervised Machine Learning in Python (Less Math, More Action!)

September 16, 2016

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If you don’t want to read about the course and just want the 88% OFF coupon code, skip to the bottom.

In recent years, we’ve seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google’s AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now “machine learning first”, meaning that machine learning is going to get a lot more attention now, and this is what’s going to drive innovation in the coming years.

Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you’re in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

The best part about this course is that it requires WAY less math than my usual courses; just some basic probability and geometry, no calculus!

In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail.

It’s important to know both the advantages and disadvantages of each algorithm we look at.

Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.

We’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.

Next we’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice.

The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.

One we’ve studied these algorithms, we’ll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.

We’ll do a comparison with deep learning so you understand the pros and cons of each approach.

We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.

We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

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

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#data science #machine learning #matplotlib #numpy #pandas #python

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

July 14, 2016

neurons

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.

Click here for 50% OFF:

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

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

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