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Deep Learning A-Z: Hands-On Artificial Neural Networks (2017)
Deep Learning A-Z: Hands-On Artificial Neural Networks (2017)
WEBRip | English | MP4 | 1280 x 720 | AVC ~48 kbps | 30 fps
AAC | 192 Kbps | 48.0 KHz | 2 channels | 21:52:04 | 3.15 GB
Genre: eLearning Video / Development, Programming

Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts.
Description
As seen on Kickstarter
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 Deepminds 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 thats why its 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.

Thats 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 thats 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Are you tired of courses based on over-used, outdated data sets?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 youre 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 didnt even exist a couple of years ago. We havent 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. Thats 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. Its 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 the target audience?
Anyone interested in Deep Learning
Students who have at least high school knowledge in math and who want to start learning Deep Learning
Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning
Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets
Any students in college who want to start a career in Data Science
Any data analysts who want to level up in Deep Learning
Any people who are not satisfied with their job and who want to become a Data Scientist
Any people who want to create added value to their business by using powerful Deep Learning tools
Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business
Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms

What Will I Learn?
Understand the intuition behind Artificial Neural Networks
Apply Artificial Neural Networks in practice
Understand the intuition behind Convolutional Neural Networks
Apply Convolutional Neural Networks in practice
Understand the intuition behind Recurrent Neural Networks
Apply Recurrent Neural Networks in practice
Understand the intuition behind Self-Organizing Maps
Apply Self-Organizing Maps in practice
Understand the intuition behind Boltzmann Machines
Apply Boltzmann Machines in practice
Understand the intuition behind AutoEncoders
Apply AutoEncoders in practice

Requirements
Just some high school mathematics level

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