ATTENTION: For Computer Scientists & Engineers Who Want To Quickly Get Up To Date With Practical Deep Learning...
All You Need To Know About Hands-On
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
Practice At-Ease With 
Hands-On Exercises Following Theory

Table of Contents:
Deep Learning and Recurrent Neural Networks Course Overview, by Guillaume Chevalier
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.

Showcase your course certificate on LinkedIn upon completion.
Recommended Background Knowledge:
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 function derivatives, multiplying matrices, and ideally also logistic regressions. 
Already having a basic understanding of Machine Learning (ML) helps, but is not required.
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 and put them into practice.
#2: Recurrent Neural Networks
Learn how Recurrent Neural Networks (RNN) works, such as the LSTM RNN. 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. 
Practical Exercises (Hands-On in Python):
#1: LSTM Human Activity Recognition

Get in-depth explanations of LSTM RNNs and how they are used for Human Activity Recognition (HAR) - a classification task with TensorFlow. 
#2: Seq2Seq Signal Forecasting
Practical exercise with Seq2Seq RNNs for predicting multiple points in the future from multiple points in the past to - a multichannel regression task with TensorFlow & Neuraxle.

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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