Deep Learning

Deep Learning

taught by Alan Blair

Aim of Course:

In this online course, you will learn about the rapidly evolving field of Deep Learning. The surge in deployed applications based on concepts and methods in this field is an indication of its potential to help fully realize the promise of Artificial Intelligence. At the end of this course you will understand the basic concepts underlying the representations and methods in deep learning and see some applications where deep learning is most effective. You will also gain an appreciation of what kind of problems are most suited for this field and current research trends.

Antiicpated Learning Outcomes:

Install and run Tensorflow
Build on the available Tensorflow code to run a linear classifier and neural net
Explain the characteristics and parameters of a convolutional network
Use Tensorflow to configure a convolutional network to recognize handwritten digits
Explain the role played by networks based on Long Short Term Memory
Explain the considerations involved in implementing both supervised and unsupervised methods in a deep network

This course may be taken individually (one-off) or as part of a certificate program.

Course Program:

WEEK 1: Neural Networks and Optimization

Overview
Machine Learning Basics
Deep Feedforward Networks
Basic Optimization Algorithms

WEEK 2: Convolutional Networks and Image Processing

Convolutional Networks
Practical Methodology
Image Applications

WEEK 3: Recurrent Architectures and Language Processing

Recurrent Networks
Long Short Term Memory
Language Applications

WEEK 4: Advanced Topics, Research Trends

Autoencoders
Representation Learning
Deep Generative Models

HOMEWORK:

Homework in this course consists of short answer questions to test concepts, practice with using public-domain tools and some guided exercises that involve freely available data. There is also an end-of-course project.

Deep learning and its place in the learning hierarchy

From Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville

Deep Learning

Who Should Take This Course:
Data scientists, statisticians, software engineers, technical managers interested in learning about what is deep learning, the state-of-the-art techniques/concepts and the range of applications in which they can be used.

Level:
Intermediate

Prerequisite:
You should have some familiarity with linear algebra, probability and information theory. The course text's chapters 2 and 3 can be read in advance to gain this background. You should also be able to run open-source tools and install them on your personal computer. You should be sufficiently familiar with Python to write programs to read/generate data and process it in appropriate data structures using relevant packages.

Organization of the Course:
Options for Credit and Recognition:

Specialization:
Specializations are an easy way for you to demonstrate mastery of a specific skill in statistics and analytics. This course is part of the Intelligence and Security Analytics Specialization which teaches statistical and machine learning methods for detecting anomalies, identifying images, and processing data from sensors.

Course Text:

The required text is Deep Learning (MIT Press, 2016) by I. Goodfellow, Y. Bengio and A. Courville.

A freely available html version is available at http://www.deeplearningbook.org/ .

It will be supplemented in the course by other technical papers, also available online.

Software:

The assignments will use TensorFlow.
Instructions for installing TensorFlow can be found here:
https://www.tensorflow.org/install/

We recommend installing Python 3 (>= 3.4) and pip3, although
TensorFlow can also be run using Python 2 (>= 2.7) and pip.
It is possible to have both versions of Python on your computer
and switch between them for different purposes.
One advantage of Python 3 is that it uses Unicode natively,
which is often convenient for language processing tasks.

If you are using a computer with a CUDA-enabled graphics card, try to
install CUDA (>=8.0) and cuDNN (>=v6) and integrate them into TensorFlow.
Otherwise, you will still be able to complete the assignments in CPU mode
(but some of the models will be run on a smaller scale).

Some assignments will build on the tutorials and software available at
https://www.tensorflow.org/get_started/

For convenience of download, it is highly recommended that you
have github installed on your computer (git is easy to install and
available for almost any operating system).

https://git-scm.com/

Instructor(s):

Dr. Alan Blair

Dates:

November 08, 2019 to December 06, 2019 November 06, 2020 to December 04, 2020

Deep Learning

Instructor(s):

Dr. Alan Blair

Dates:
November 08, 2019 to December 06, 2019 November 06, 2020 to December 04, 2020

Course Fee: $549

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

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The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).

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Deep Learning

taught by Alan Blair

WEEK 1: Neural Networks and Optimization

WEEK 2: Convolutional Networks and Image Processing

WEEK 3: Recurrent Architectures and Language Processing

WEEK 4: Advanced Topics, Research Trends

Deep learning and its place in the learning hierarchy

Deep Learning

Deep Learning

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