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  5. Getting Started With Deep Learning: Recurrent Neural Networks

Introduction

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Catalit

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Contents

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Introduction
Overview
Transcript
DifficultyBeginner
Duration45m
Students64
Ratings
4.5/5
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Description

From the internals of a neural net to solving problems with neural networks to understanding how they work internally, this course expertly covers the essentials needed to succeed in machine learning.

This course moves on from cloud computing power and covers Recurrent Neural Networks. Learn how to use recurrent neural networks to train more complex models.

Understand how models are built to allow us to treat data that comes in sequences. Examples of this could include unstructured text, music, and even movies.

This course is comprised of 9 lectures with 2 accompanying exercises.

Learning Objective

  • Understand how recurrent neural network models are built
  • Learn the various applications of recurrent neural networks

Prerequisites

 

About the Author

Students1228
Courses8
Learning paths3

I am a Data Science consultant and trainer. With Catalit I help companies acquire skills and knowledge in data science and harness machine learning and deep learning to reach their goals. With Data Weekends I train people in machine learning, deep learning and big data analytics. I served as lead instructor in Data Science at General Assembly and The Data Incubator and I was Chief Data Officer and co-­founder at Spire, a Y-Combinator-­backed startup that invented the first consumer wearable device capable of continuously tracking respiration and activity. I earned a joint PhD in biophysics at University of Padua and Université de Paris VI and graduated from Singularity University summer program of 2011.   

Hello and welcome to section eight on recurrent neural networks. In the last section, we talked about cloud computing power and how to use it to train more complex models. In this section, we will build models that are able to treat data that comes in sequences. This could be, for example, unstructured text, or music, or even movies. We will also learn that these models can generate continuous predictions from one single input. These models are called recurrent neural networks and we will learn a couple of formulations, including GRUs or gated recurrent units and long short-term memory networks, LSTMs for short. Recurrent neural networks have a lot of applications and we'll see a bunch of them in this chapter, including anomaly detection, generation of data, and more. So let's get started with section eight on recurrent neural networks.