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. 
Watch The Free Course Preview Now!
Enter your information below to access the three first sections of the table of contents.
The preview will remain available for you to watch for:
countdown
00Hours00Minutes00Seconds


Provided by Neuraxio
Copyright © 2021 - Neuraxio Inc. 

Powered By ClickFunnels.com