3 days ago
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Deep Learning and 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.
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.
What you’ll 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
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