This course covers the concepts of recurrent and convolutional nets (theory and practice), natural signals properties and the convolution, and recurrent neural networks (vanilla and gated, LSTM). It is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this module include: Introduction to Deep Learning (module 1 of the course) and an Introduction to Data Science or a Graduate Level Machine Learning course.
Deep Learning: Parameters Sharing
Course Features
Videos of lectures
Lecture slides
Jupyter notebooks
Homework exercises
Tutorials
Lessons of this Course
1
1
Duration:
1:59:47
Speaker:
This lecture covers the concept of parameter sharing: recurrent and convolutional nets and is a part of the Deep Learning Course at NYU's Center for Data Science.
2
2
Duration:
51:40
Speaker:
This lecture covers the concept of convolutional nets in practice and is a part of the Deep Learning Course at NYU's Center for Data Science.
3
3
Duration:
1:09:12
Speaker:
This lecture discusses the concept of natural signals properties and the convolutional nets in practice and is a part of the Deep Learning Course at NYU's Center for Data Science.
4
4
Duration:
1:05:36
Speaker:
This lecture covers the concept of recurrent neural networks: vanilla and gated (LSTM) and is a part of the Deep Learning Course at NYU's Center for Data Science.
