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'll review Exercise 1, of Section 8. Where we ask you to perform a reshaping of the inputs in the windowed time-series prediction that we've done in class. Essentially, what we want to do, is pass to the LSTM, the 12 months, as a sequence of single months. Basically what we did in class, was to take the 12 months, and pass them as a vector with 12 coordinates, so, we passed the months in parallel and so we really didn't exploit the LSTM memory, we just passed the whole vector at once. What we're gonna do here, is pass the months one at a time, it's actually pretty simple, you'll have to reshape, x_train and x_test so that they represent a set of sequences instead of a set of vectors. And retrain the same LSTMmodel, adapting the input_shape. Compare the performance with the previous model and tell us if it's better or worse. 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.