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Deep Learning A-Z™

Hands-On Artificial Neural Networks

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Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role.

But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.

Why Deep Learning A-Z?

Here are five reasons we think Deep Learning A-Z™ really is different, and stands out from the crowd of other training programs out there:

1. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it.

That's why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning.

2... But they forget to explain, perhaps, the most important part: why you are doing what you are doing. And that's how this course is so different. We focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms.

With our intuition tutorials you will be confident that you understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see for yourself how much more meaningful your experience will be. This is a game-changer.

3.

Yes? Well then you're in for a treat.

Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges:

  • Artificial Neural Networks to solve a Customer Churn problem
  • Convolutional Neural Networks for Image Recognition
  • Recurrent Neural Networks to predict Stock Prices
  • Self-Organizing Maps to investigate Fraud
  • Boltzmann Machines to create a Recomender System
  • Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize

*Stacked Autoencoders is a brand new technique in Deep Learning which didn't even exist a couple of years ago. We haven't seen this method explained anywhere else in sufficient depth.

4. HANDS- Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.

In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after.

This is a course which naturally extends into your career.

5. IN-

Well, this course is different. We are fully committed to making this the most disruptive and powerful Deep Learning course on the planet. With that comes a responsibility to constantly be there when you need our help.

In fact, since we physically also need to eat and sleep we have put together a team of professional Data Scientists to help us out. Whenever you ask a question you will get a response from us within 48 hours maximum.

No matter how complex your query, we will be there. The bottom line is we want you to succeed.

The Tools

Tensorflow and Pytorch are the two most popular open-source libraries for Deep Learning. In this course you will learn both.

TensorFlow was developed by Google and is used in their speech recognition system, in the new google photos product, gmail, google search and much more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber and dozens more.

PyTorch is as just as powerful and is being developed by researchers at Nvidia and leading universities: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce and Facebook.

So which is better and for what?

Well, in this course you will have an opportunity to work with both and understand when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials we compare the two and give you tips and ideas on which could work best in certain circumstances.

The interesting thing is that both these libraries are barely over 1 year old. That's what we mean when we say that in this course we teach you the most cutting edge Deep Learning models and techniques.

More Tools

Theano is another open source deep learning library. It's very similar to Tensorflow in its functionality, but nevertheless we will still cover it.

Keras is an incredible library to implement Deep Learning models. It acts as a wrapper for Theano and Tensorflow. Thanks to Keras we can create powerful and complex Deep Learning models with only a few lines of code. This is what will allow you to have a global vision of what you are creating. Everything you make will look so clear and structured thanks to this library, that you will really get the intuition and understanding of what you are doing.

Even More Tools

Scikit-learn the most practical Machine Learning library. We will mainly use it:

  • to evaluate the performance of our models with the most relevant technique, k-Fold Cross Validation
  • to improve our models with effective Parameter Tuning
  • to preprocess our data, so that our models can learn in the best conditions

And of course, we have to mention the usual suspects. This whole course is based on Python and in every single section you will be getting hours and hours of invaluable hands-on practical coding experience.

Plus, throughout the course we will be using Numpy to do high computations and manipulate high dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets the most efficiently.

Who Is This Course For?

As you can see, there are lots of different tools in the space of Deep Learning and in this course we make sure to show you the most important and most progressive ones so that when you're done with Deep Learning A-Z™ your skills are on the cutting edge of today's technology.

If you are just starting out into Deep Learning, then you will find this course extremely useful. Deep Learning A-Z™ is structured around special coding blueprint approaches meaning that you won't get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident.

If you already have experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning A-Z™ you will master some of the most cutting-edge Deep Learning algorithms and techniques (some of which didn't even exist a year ago) and through this course you will gain an immense amount of valuable hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications.

Real-World Case Studies

Mastering Deep Learning is not just about knowing the intuition and tools, it's also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That's why in this course we are introducing six exciting challenges:

#1 Churn Modelling Problem

In this part you will be solving a data analytics challenge for a bank. You will be given a dataset with a large sample of the bank's customers. To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank.

Your goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Besides, you are asked to rank all the customers of the bank, based on their probability of leaving. To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach.

If you succeed in this project, you will create significant added value to the bank. By applying your Deep Learning model the bank may significantly reduce customer churn.

#2 Image Recognition

In this part, you will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show you how to do it - by simply changing the pictures in the input folder.

For example, you will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if you want to keep it fitted to cats and dogs, then you will literally be able to a take a picture of your cat or your dog, and your model will predict which pet you have. We even tested it out on Hadelin’s dog.

#3 Stock Price Prediction

In this part, you will create one of the most powerful Deep Learning models. We will even go as far as saying that you will create the Deep Learning model closest to “Artificial Intelligence”. Why is that? Because this model will have long-term memory, just like us, humans.

The branch of Deep Learning which facilitates this is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. We are extremely excited to include these cutting-edge deep learning methods in our course.

In this part you will learn how to implement this ultra-powerful model, and we will take the challenge to use it to predict the real Google stock price. A similar challenge has already been faced by researchers at Stanford University and we will aim to do at least as good as them.

#4 Fraud Detection

According to a recent report published by Markets & Markets the Fraud Detection and Prevention Market is going to be worth $33.19 Billion USD by 2021. This is a huge industry and the demand for advanced Deep Learning skills is only going to grow. That’s why we have included this case study in the course.

This is the first part of Volume 2 - Unsupervised Deep Learning Models. The business challenge here is about detecting fraud in credit card applications. You will be creating a Deep Learning model for a bank and you are given a dataset that contains information on customers applying for an advanced credit card.

This is the data that customers provided when filling the application form. Your task is to detect potential fraud within these applications. That means that by the end of the challenge, you will literally come up with an explicit list of customers who potentially cheated on their applications.

#5 & 6 Recommender Systems

From Amazon product suggestions to Netflix movie recommendations - good recommender systems are very valuable in today's World. And specialists who can create them are some of the top-paid Data Scientists on the planet.

We will work on a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched. The ratings go from 1 to 5, exactly like in the Netflix dataset, which makes the Recommender System more complex to build than if the ratings were simply “Liked” or “Not Liked”.

Your final Recommender System will be able to predict the ratings of the movies the customers didn’t watch. Accordingly, by ranking the predictions from 5 down to 1, your Deep Learning model will be able to recommend which movies each user should watch. Creating such a powerful Recommender System is quite a challenge so we will give ourselves two shots. Meaning we will build it with two different Deep Learning models.

Our first model will be Deep Belief Networks, complex Boltzmann Machines that will be covered in Part 5. Then our second model will be with the powerful AutoEncoders, my personal favorites. You will appreciate the contrast between their simplicity, and what they are capable of.

And you will even be able to apply it to yourself or your friends. The list of movies will be explicit so you will simply need to rate the movies you already watched, input your ratings in the dataset, execute your model and voila. The Recommender System will tell you exactly which movies you would love one night you if are out of ideas of what to watch on Netflix.

Summary

In conclusion, this is an exciting training program filled with intuition tutorials, practical exercises and real-World case studies.

We are super enthusiastic about Deep Learning and hope to see you inside the class.

Kirill & Hadelin

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Udemy

Rating 4.3 based on 5,513 ratings
Length 23 hours
Starts On Demand (Start anytime)
Cost $10
From Udemy
Instructors Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team
Download Videos Only via the Udemy mobile app
Language English
Subjects Business Data Science
Tags Business Data & Analytics

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What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

auto encoders in 14 reviews

Something that at least can be converted to features encoder, like convolutional neural networks (we can use CAE) or ordinary auto encoders.

boltzmann machine and auto encoders (highly disappointing) This course is unfortunately covering the advertised topics only in a very superficial way - buzzwords for marketing reasons without any valuable content.

ANN and CNN has been explained superbly ... so is SOM ... but RNN and Auto Encoders were difficult to comprehend , especially the RNN , rest definitely worth it , too good to start off Very light intro to deep learning and I did not have any difficulties understanding majority of the materials.

I really like that the Boltzmann Machines and Auto Encoders are also covered here.

I wish they had supplemented their code-videos (especially: Recurrent Neural Networks, Boltzman Machines & Auto Encoders) with some written material/flow-charts/algorithms...

good The course was good for understanding neural networks (basic neural nets, convolutional, and recurrent) but it began to lose me when it went into Boltzmann machines and Auto Encoders.

However, i didn't realize what is the advantage of auto encoders over ANN/RNN .

boltzmann machine and auto encoders (highly disappointing) I expected to start with a very basic 10,000 foot overview of "Deep Learning" and this course provided that.

andrew ng in 12 reviews

We can not compare this course with Andrew Ng deep learning but this course is good If you want learn quicky and some code.

Hence, have to spend a significant time outside the course material to get a better understanding of concepts Recommend Andrew NG for a more fundamental and mathematical understanding of the concepts and then using this course for practical implementation It is very complete and easy to understand, but it is very repetitive (if I need to hear the same explanation again, I could just do it myself) Your code is not refactored also it is not explained that much.

Neural Networks explained pretty well, Andrew Ng's Machine Learning on Coursera is a great course but I found NN better explained here, without a doubt 5 stars it is!

As a graduate of Prof. Andrew Ng's ML and DL classes on Coursera, the bar has been set pretty high.

Recommended for those whom have an understanding on Machine Learning algorithm who want to make the leap to Deep Learning A lot of it is similar to Andrew Ng's Machine Learning course in the beginning, but I am excited for the more complex information, like Boltzmann Machines.

If I hadn't done Python before and had some experience of Machine Learning (Andrew Ng, Coursera) I would be struggling.

Hence, have to spend a significant time outside the course material to get a better understanding of concepts Recommend Andrew NG for a more fundamental and mathematical understanding of the concepts and then using this course for practical implementation Love the way it is progressing and the interconnection with real world So far so good.

dealing with data (based on sheets) and basic basic deep learning concepts As a graduate of Prof. Andrew Ng's ML and DL classes on Coursera, the bar has been set pretty high.

artificial intelligence a-z in 10 reviews

And I am looking forward to start my next course, Artificial Intelligence A-Z: How to build an AI.

Looking forward to the Artificial Intelligence A-Z (can't stop doing your course.. haha !!)Cheers!

I like the "code step by step from scratch" approach, which the instructors' another course, Artificial Intelligence A-Z doesn't have.

Awesome training for Deep Learning, looking forward to join Artificial Intelligence A-Z course.

[Alternative UDEMY course which is worth the money and explains the process] Artificial Intelligence A-Zâ„¢: Learn How To Build An AI Didn't really need the history lesson - I'm already familiar with it.

[Alternative UDEMY course which is worth the money and explains the process] Artificial Intelligence A-Z™: Learn How To Build An AI Good for beginers Looks Interesting at this moment after the first lecture.

hadelin de ponteves in 10 reviews

I Love the intuition, it's a very nice approach as it gave me the foundation to understand it, love it and i'll buy any of his courses Thank you so much Kirill Eremenko, Hadelin de Ponteves and SuperDatascience team for this comprehensive course on Deep Learning.

Thanks to Kirill Eremenko and Hadelin de Ponteves.

I had no doubt about Kirill Eremenko’s and Hadelin de Ponteves teaching skills and I contributed through Kickstarter for this course.I am happy to see the outcome.

I really appreciate Kirill Eremenko and Hadelin de Ponteves for putting all the efforts when making this beautiful course.

Thanks Kirill Eremenko and Hadelin de Ponteves Coming from a Mechanical Engineering background, I think the course well organized by guiding you step-by-step through the process and, having the templates is really helpful.

Thank you so much Kirill Eremenko, Hadelin de Ponteves and SuperDatascience team for this comprehensive course on Deep Learning.

Thanks Kirill Eremenko and Hadelin de Ponteves Implementation wise course give the good idea, Some more mathematical information will help to understanding model deeply.

muito bom in 10 reviews

Muito bom curso.

Muito bom.

O curso é muito bom e bastante prático, além de indicar boas referências para consultas futuras.

Though slides are great and teacher is clear It would be better if there is some transcript in the videos muito bom até agr, mas ainda estou no começo Generic stuff, but definitely heading in the right direction Well explained, interesting material Excellent, and fulfills my needed, but if you needed to expertise underlying mathematics, then this is not suitable.

Very comprehensive Solid communication Great :) very good good subtitles VERY GOOD Great stuff Muito bom!

Wondeful experience :) Muito bom curso.

We need more examples Muito bom.

Nice Experience to learn such a tough Topic of Neural Network.. O curso é muito bom e bastante prático, além de indicar boas referências para consultas futuras.

wide range in 8 reviews

Recommend The instructors cover a wide range of topics and expose students to many uses of deep learning so I feel motivated after completing the course to go and explore further.

The exposure to a wide range of techniques was most valuable and the step by step code creation with explanations made it even more so.

The course is really nice and covers a very wide range of subjects..

Good for start and practise on example with wide range of neural network.

The instructors cover a wide range of topics and expose students to many uses of deep learning so I feel motivated after completing the course to go and explore further.

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Deep Learning Research Scientist $86k

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Deep Learning R&D Engineer $127k

Learning Assitant $142k

Deep Submergence Systems Program Manager $157k

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Udemy

Rating 4.3 based on 5,513 ratings
Length 23 hours
Starts On Demand (Start anytime)
Cost $10
From Udemy
Instructors Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team
Download Videos Only via the Udemy mobile app
Language English
Subjects Business Data Science
Tags Business Data & Analytics

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