Zero to Deep Learning Bootcamp Three - Working with Convolutional and Recurrent Neural Networks

Developed with Catalit
OverviewStepsAuthor
Catalit
This content is developed in partnership with Catalit
DifficultyAdvanced
Duration3h 32m
Students81
Ratings
5/5
star star star star star

Description

Introduction

This Learning Path is the third and final of three Learning Paths in the Zero to Deep Learning Bootcamp Cloud Academy has developed in collaboration with Deep Learning expert Francesco Mosconi from Calalit. The Zero to Deep Learning Bootcamp has been developed to help you master Working with Convolutional and Recurrent Neural Networks in an interactive, self paced format. 

Intended Audience

Anyone interested in working with Convolution and Recurrent Neural Networks. Whether you're just starting out with machine learning or a more experienced data scientist looking to throw deep learning into the mix, this Learning Path will provide the necessary skills to serve as a solid foundation for you to continue learning after the course has been completed.

Learning Objectives

  • Understand the difference between Convolutional and Recurrent Neural Networks
  • Recognize and explain the concepts of both Convolutional and Recurrent Neural Networks
  • Analyze the best ways to Improve Performance within the network environment.

Prerequisites

Course Agenda

  • This Learning Path is made up of 3 expertly instructed Courses along with a final Exam to test what you have learned.

Feedback

We welcome all feedback so please direct any comments or questions on this course to us at support@cloudacademy.com.

Certificate

Your certificate for this learning path
lock

Learning Path Steps

1 courses

In this course, discover convolutions and the convolutional neural networks involved in Data and Machine Learning. Introducing the concept of tensor, which is essential for everything that follows. Learn to apply the right kind of data such as images. Imag...

2 courses

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 cov...

3 courses

Move on from what you learned from studying the principles of recurrent neural networks, and how they can solve problems involving sequencing, with this cohesive course on Improving Performace. Learn to improve the performance of your neural networks by sta...

4 exam-filled

Exam: Zero to Deep Learning- Bootcamp Three - Working with Convolutional and Recurrent Neural Networks

About the Author

Students1209
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.