ATTENTION: For Computer Scientists Who Wants to Quickly get up to date with deep learning...
All You Need To Know About
Deep Learning 
Recurrent neural networks
To Quickly Get Up To Date With Deep Learning
Deep Learning and Recurrent Neural Networks Course Overview, by Guillaume Chevalier

Table of Contents:
Deep Learning and Recurrent Neural Networks Course Overview, by Guillaume Chevalier
Core Skills That You'll Develop:
#1: Artificial Neural Networks' Basics

Starting with the basics of artificial neural networks and deep neural networks, you'll be able to quickly grasp how things works.
#2: Recurrent Neural Networks
Learn how Recurrent Neural Networks (RNN), works. This knowledge allows for learning more abstract NN architectures afterwards.
#3: Complex DL Architectures
Get to know several deep learning techniques to make good decisions. 
This training should satisfy you if you have at least basic python programming skills, and good basic university-level mathematic skills such as an understanding of derivatives and multiplying matrices. 

To boost your learning, this is the most richly dense, accelerated course on the topic of Deep Learning & Recurrent Neural Networks (DL & RNNs), such as Seq2Seq, LSTMs, RNNs and Attention Mechanisms. Learn advanced time series processing with Deep Learning. 

Plus, learn how this technology can help you solve your specific problem - learn by examples with real, concrete examples with TensorFlow in Python.
The Author

Guillaume Chevalier

Author of machine learning open-source projects that collectively received more than 5700 stars on GitHub, Guillaume has been a speaker at more than 25 events in the past few years. 
Plus, he worked on more than 57 artificial intelligence projects for more than 15 companies. 
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