This module provides an introduction to the motivation of deep learning and its history and inspiration. The module covers the concepts of: gradient descent and the back propagation algorithm, neural nets inference, modules and architectures, an neural nets training. 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 Data Science or a Graduate Level Machine Learning.
Introduction to Deep Learning
Course Features
Videos
Lectures
Slides
Jupyter notebooks
Homework exercises
Tutorials
Lessons of this Course
1
3
Duration:
50:17
Speaker:
This is the Introductory Module to 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.
2
4
Duration:
1:51:03
Speaker:
This module covers the concepts of gradient descent and the backpropagation algorithm and is a part of the Deep Learning Course at NYU's Center for Data Science.
3
5
Duration:
1:01:53
Speaker:
This lecture covers concepts associated with neural nets, including rotation and squashing, and is a part of the Deep Learning Course at New York University's Center for Data Science (CDS).
4
6
Duration:
1:42:26
Speaker:
This lesson provides a detailed description of some of the modules and architectures involved in the development of neural networks.
5
7
Duration:
1:05:47
Speaker:
This lecture covers the concept of neural nets training (tools, classification with neural nets, and PyTorch implementation) and is a part of the Deep Learning Course at NYU's Center for Data Science.
