Recurrent Neural Networks
The course is part of this learning path
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.
- Understand how recurrent neural network models are built
- Learn the various applications of recurrent neural networks
- It is recommended to complete the Introduction to Data and Machine Learning course before starting.
Hey guys, welcome back. In this video, we are going to review Exercise 2 of Section 8, in particular, in this exercise, we will ask you to apply RNN's to images. In fact, RNN's we've learned then, when talking about sequences, but they can clearly be applied to the images as well. And so were asking you to reload the MNIST data set, by now you should know by heart, and reshape it so the the image looks like a long sequence of pixels, which is the same thing we did for the fully connected model. Then create a recurrent model and train it on the training data. Finally you have to compare the performance of the fully connected with the performance of this model and with the performance of the Convolutional model. This exercise will require quite a bit of computational powers, so feel free to run this on a GPU on flight, if it's too slow on your laptop. Thank you for watching. And good luck.
About the Author
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.