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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. ROBUST STRUCTURE
The first and most important thing we focused on is giving the course a robust structure. 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. INTUITION TUTORIALS
So many courses and books just bombard you with the theory, and math, and coding… 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. EXCITING PROJECTS
Are you tired of courses based on over-used, outdated data sets?
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-ON CODING
In Deep Learning A-Z™ we code together with you. 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-COURSE SUPPORT
Have you ever taken a course or read a book where you have questions but cannot reach the author?
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.
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