Deep Learning

Identify and build the right neural network for solving your biggest challenges today—and in the future.

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Course Dates

STARTS ON

February 15, 2023

Course Duration

DURATION

10 weeks, online
10-15 hours per week

Course Fee
Course Fee

For Your Team

Enroll your team and learn with your peers

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Team-Based Learning Options

  • Enroll as a team or group and learn with your peers

  • Receive support and services

  • Inquire about special team/group pricing

  • ENROLL YOUR TEAM

    Emeritus works with leading companies to close critical skills gaps


    Our partners include

    JP Morgan
    Amazon
    AB in Bev

Go Deeper With Carnegie Mellon University Deep Learning Skills for What’s Next

Expertise in deep learning is an in-demand skill for technical positions in software engineering and data science. Applications of artificial networks are wide-reaching and include solutions for problems in the language (speech recognition, translation), transportation (autonomous driving, real-time analysis), imaging (disease diagnosis, facial recognition), and many more areas across sports, and the healthcare industry.

Over the course of 10 weeks, you will gain an understanding of how neural networks operate and how to identify the right architecture for addressing your current and future challenges.

Carnegie Mellon University Is Ranked #1 in Artificial Intelligence (AI) Specialty and Graduate Programs for Computer Science

SOURCE: U.S. NEWS & WORLD REPORT

Key Outcomes

This 10-week online program is designed to provide software engineers, developers, data scientists, and AI and ML (Machine Learning) professionals with deeper technical skills, allowing you to solve more complex challenges and add more value to your organization.

  • Develop an understanding of deep learning techniques
  • Understand the structure, function, and training of key neural network architectures for building tools and systems
  • Build the confidence to apply deep learning methods to real-world problems

Program Modules

Module 1:

Introduction and Universal Approximation

Receive an introduction to the foundational concepts of neural networks, the basic architecture of a neuron, and the history of the field.

Module 2:

Training Multilayer Perceptrons

Explore the concept of learning as it relates to multilayer perceptrons, including model parameters, gradients, and loss functions.

Module 3:

Stochastic Gradient Descent and Optimizers

Learn about the use of incremental updates and methods for tuning convergence for optimal model performance.

Module 4:

Basics of Convolutional Neural Networks (CNNs)

Explore convolution and its role in ensuring that neural networks are invariant with respect to target pattern location and also how shared parameters decrease computational complexity, leading to model performance gains.

Module 5:

CNNs: Training and Variants

This module examines CNN layers that perform a variety of operations, including alternating pooling and convolution.

The time allotted to complete assignments has been extended after week 5.

Module 6:

Basics of Recurrent Neural Networks (RNNs)

Explore RNNs and the types of problems that are better suited for these types of models.

Module 7:

Connectionist Temporal Classification and Sequence-to-Sequence Models

Learn about variable-timing sequence problems and the sequence-to-sequence model used for translation problems.

Module 8:

Attention and Translation

Delve into deeper concepts related to natural language processing (NLP) and the use of encoder-decoder networks for translation.

Module 9:

Representations and Autoencoders

Consider more deeply what networks learn at each layer.

Module 10:

Transformers and Graph Networks, Variational Autoencoders, Generative Adversarial Networks

Explore additional topics in deep learning, gaining practical information that prepares you for the next phase of your deep learning journey.

Module 1:

Introduction and Universal Approximation

Receive an introduction to the foundational concepts of neural networks, the basic architecture of a neuron, and the history of the field.

Module 6:

Basics of Recurrent Neural Networks (RNNs)

Explore RNNs and the types of problems that are better suited for these types of models.

Module 2:

Training Multilayer Perceptrons

Explore the concept of learning as it relates to multilayer perceptrons, including model parameters, gradients, and loss functions.

Module 7:

Connectionist Temporal Classification and Sequence-to-Sequence Models

Learn about variable-timing sequence problems and the sequence-to-sequence model used for translation problems.

Module 3:

Stochastic Gradient Descent and Optimizers

Learn about the use of incremental updates and methods for tuning convergence for optimal model performance.

Module 8:

Attention and Translation

Delve into deeper concepts related to natural language processing (NLP) and the use of encoder-decoder networks for translation.

Module 4:

Basics of Convolutional Neural Networks (CNNs)

Explore convolution and its role in ensuring that neural networks are invariant with respect to target pattern location and also how shared parameters decrease computational complexity, leading to model performance gains.

Module 9:

Representations and Autoencoders

Consider more deeply what networks learn at each layer.

Module 5:

CNNs: Training and Variants

This module examines CNN layers that perform a variety of operations, including alternating pooling and convolution.

The time allotted to complete assignments has been extended after week 5.

Module 10:

Transformers and Graph Networks, Variational Autoencoders, Generative Adversarial Networks

Explore additional topics in deep learning, gaining practical information that prepares you for the next phase of your deep learning journey.

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Program Experience

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Office Hours With Learning Facilitators

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Three Two-part Graded Programming Assignments

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Graphic-Rich Lecture Videos

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Knowledge Checks

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Dedicated Program Support Team

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Mobile Learning App

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Peer Discussion

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Capstone Project

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Bonus Content on Advanced Topics

Who Should Attend?

This program is designed for participants seeking a more sophisticated understanding of neural network architectures and the confidence to deploy their skills to solve real-world artificial intelligence problems. This program is most suitable for:

  • Software Engineers
  • Software Developers
  • Data Scientists & Teams
  • AI & ML Professionals
  • Technology Professionals

Prerequisites: The subject matter in this program is rigorous. To ensure success, participants must have a strong working knowledge of linear algebra, calculus, statistics, probability, and object-oriented programming including Python.

Faculty

Bhiksha Raj

Professor, Language Technologies Institute, School of Computer Science, Carnegie Mellon University

Bhiksha Raj works on a variety of areas related to AI, with a focus on speech processing, and more generally on intelligent systems that can learn to understand and respond to their acoustic environment... More info

Rita Singh

Professor, School of Computer Science, Carnegie Mellon University

Rita Singh works on research in the area of AI-based techniques for advanced human sensing and is currently focused on AI-driven voice forensics. She has worked in the areas of speech and audio processing for more than two decades... More info

Certificate

Example image of certificate that will be awarded after successful completion of this program

Certificate

Upon successful completion of the program, participants will receive a verified digital certificate of completion from Carnegie Mellon University School of Computer Science Executive Education. This is a training program and it is not eligible for academic credit.

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Your digital certificate will be issued in your legal name and emailed to you at no additional cost upon completion of the program, per the stipulated requirements. All certificate images are for illustrative purposes only and may be subject to change at the discretion of Carnegie Mellon University’s School of Computer Science Executive Education.

The Carnegie Mellon School of Computer Science Executive Education learning experience

At Carnegie Mellon’s Executive Education Program in the School of Computer Science, we provide organizations and people access to the skills and tools necessary to solve real world technical problems by equipping the next generation of technology leaders with the experience, insights, and novel solutions developed by our community of computer science experts. From custom training programs to online individualized learning, our cutting-edge programming—backed by faculty who pioneered the field—takes your skillset to the next level, giving you the tools to tackle your company’s next great technological challenge.

FAQs

  • How do I know if this program is right for me?

    After reviewing the information on the program landing page, we recommend you submit the short form above to gain access to the program brochure, which includes more in-depth information. If you still have questions on whether this program is a good fit for you, please email learner.success@emeritus.org, and a dedicated program advisor will follow-up with you very shortly.


    Are there any prerequisites for this program?

    Some programs do have prerequisites, particularly the more technical ones. This information will be noted on the program landing page, as well as in the program brochure. If you are uncertain about program prerequisites and your capabilities, please email us at learner.success@emeritus.org for assistance.


    Note that, unless otherwise stated on the program web page, all programs are taught in English and proficiency in English is required.


    What is the typical class profile?

    More than 50 percent of our participants are from outside the United States. Class profiles vary from one cohort to the next, but, generally, our online certificates draw a highly diverse audience in terms of professional experience, industry, and geography — leading to a very rich peer learning and networking experience.


    What other dates will this program be offered in the future?

    Check back to this program web page or email us at learner.success@emeritus.org to inquire if future program dates or the timeline for future offerings have been confirmed yet.

  • How much time is required each week?

    Each program includes an estimated learner effort per week. This is referenced at the top of the program landing page under the Duration section, as well as in the program brochure, which you can obtain by submitting the short form at the top of this web page.



    How will my time be spent?

    We have designed this program to fit into your current working life as efficiently as possible. Time will be spent among a variety of activities including:



    • Engaging with recorded video lectures from faculty
    • Attending webinars and office hours, as per the specific program schedule
    • Reading or engaging with examples of core topics
    • Completing knowledge checks/quizzes and required activities
    • Engaging in moderated discussion groups with your peers
    • Completing your final project, if required

    The program is designed to be highly interactive while also allowing time for self-reflection and to demonstrate an understanding of the core topics through various active learning exercises. Please contact us at learner.success@emeritus.org if you need further clarification on program activities.



    What is it like to learn online with the learning collaborator, Emeritus?

    More than 250,000 professionals globally, across 80 countries, have chosen to advance their skills with Emeritus and its educational learning partners. In fact, 90 percent of the respondents of a recent survey across all our programs said that their learning outcomes were met or exceeded.

    A dedicated program support team is available 24/5 (Monday to Friday) to answer questions about the learning platform, technical issues, or anything else that may affect your learning experience.


    How do I interact with other program participants?

    Peer learning adds substantially to the overall learning experience and is an important part of the program. You can connect and communicate with other participants through our learning platform.

  • What are the requirements to earn the certificate?

    Each program includes an estimated learner effort per week, so you can gauge what will be required before you enroll. This is referenced at the top of the program landing page under the Duration section, as well as in the program brochure, which you can obtain by submitting the short form at the top of this web page. All programs are designed to fit into your working life.

    This program is scored as a pass or no-pass; participants must complete the required activities to pass and obtain the certificate of completion. Some programs include a final project submission or other assignments to obtain passing status. This information will be noted in the program brochure. Please contact us at learner.success@emeritus.org if you need further clarification on any specific program requirements.


    What type of certificate will I receive?

    Upon successful completion of the program, you will receive a smart digital certificate. The smart digital certificate can be shared with friends, family, schools, or potential employers. You can use it on your cover letter, resume, and/or display it on your LinkedIn profile.
    The digital certificate will be sent approximately two weeks after the program, once grading is complete.


    Can I get the hard copy of the certificate?

    No, only verified digital certificates will be issued upon successful completion. This allows you to share your credentials on social platforms such as LinkedIn, Facebook, and Twitter.


    Do I receive alumni status after completing this program?

    No, there is no alumni status granted for this program. In some cases, there are credits that count toward a higher level of certification. This information will be clearly noted in the program brochure.


    How long will I have access to the learning materials?

    You will have access to the online learning platform and all the videos and program materials for 12 months following the program start date. Access to the learning platform is restricted to registered participants per the terms of agreement.

  • What equipment or technical requirements are there for this program?

    Participants will need the latest version of their preferred browser to access the learning platform. In addition, Microsoft Office and a PDF viewer are required to access documents, spreadsheets, presentations, PDF files, and transcripts.


    Do I need to be online to access the program content?

    Yes, the learning platform is accessed via the internet, and video content is not available for download. However, you can download files of video transcripts, assignment templates, readings, etc. For maximum flexibility, you can access program content from a desktop, laptop, tablet, or mobile device.

    Video lectures must be streamed via the internet, and any livestream webinars and office hours will require an internet connection. However, these sessions are always recorded, so you may view them later.

  • Can I still register if the registration deadline has passed?

    Yes, you can register up until seven days past the published start date of the program without missing any of the core program material or learnings.


    What is the program fee, and what forms of payment do you accept?

    The program fee is noted at the top of this program web page and usually referenced in the program brochure as well.

    • Flexible payment options are available (see details below as well as at the top of this program web page next to FEE).
    • Tuition assistance is available for participants who qualify. Please email learner.success@emeritus.org.

    What if I don’t have a credit card? Is there another method of payment accepted?

    Yes, you can do the bank remittance in the program currency via wire transfer or debit card. Please contact your program advisor, or email us at learner.success@emeritus.org for details.


    I was not able to use the discount code provided. Can you help?

    Yes! Please email us at learner.success@emeritus.org with the details of the program you are interested in, and we will assist you.


    How can I obtain an invoice for payment?

    Please email learner.success@emeritus.org with your invoicing requirements and the specific program you’re interested in enroling in.


    Is there an option to make flexible payments for this program?

    Yes, the flexible payment option allows a participant to pay the program fee in installments. This option is made available on the payment page and should be selected before submitting the payment.


    How can I obtain a W9 form?

    Please email us at learner.success@emeritus.org for assistance.

  • What is the policy on refunds and withdrawals?

    You may request a full refund within seven days of your payment or 14 days after the published start date of the program, whichever comes later. If your enrolment had previously been deferred, you will not be entitled to a refund. Partial (or pro-rated) refunds are not offered. All withdrawal and refund requests should be sent to admissions@emeritus.org.



    What is the policy on deferrals?

    After the published start date of the program, you have until the midpoint of the program to request to defer to a future cohort of the same program. A deferral request must be submitted along with a specified reason and explanation. Cohort changes may be made only once per enrolment and are subject to availability of other cohorts scheduled at our discretion. This will not be applicable for deferrals within the refund period, and the limit of one deferral per enrolment remains. All deferral requests should be sent to admissions@emeritus.org.

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Early registrations are encouraged. Seats fill up quickly!