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.
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.
- 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.
- We recommend completing the Zero to Deep Learning Bootcamp One - Introduction to Data Science and Machine Learning Learning Path prior to starting this Learning Path.
- This Learning Path is made up of 3 expertly instructed Courses along with a final Exam to test what you have learned.
We welcome all feedback so please direct any comments or questions on this course to us at email@example.com.
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.