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Introduction to Deep Learning


Dive deep into the fundamental theoretical and practical topics related to deep learning.

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  • Estimated time
    1 month

  • Enroll by
    June 14, 2023

    Get access to classroom immediately on enrollment

  • Skills acquired
    Neural Networks, Training Neural Networks, Perceptron

What You Will Learn

  1. Introduction to Deep Learning

    1 month to complete

    Learn how experts think about deep learning, when it is appropriate to use deep learning, and how to apply the skill. Then learn the foundational algorithms underpinning modern deep learning: gradient descent and backpropagation. Once those foundations are established, explore design constructs of neural networks and the impact of these design decisions. Finally, explore how neural network training can be optimized for accuracy and robustness using training techniques like early stopping, dropout, regularization, and momentum. Throughout the course, theory and fundamental implementations are woven together with PyTorch code to reinforce both the theory and practice of deep learning.

    Prerequisite knowledge

    Intermediate Python

    1. Deep Learning

      Explain the difference between artificial intelligence, machine learning, and deep learning. Recognize the power of deep learning by reviewing popular examples of deep learning applications

      • Minimizing the Error Function with Gradient Descent

        Use PyTorch to preprocess data and use maximum likelihood, cross-entropy, and probability to measure model performance. Apply gradient descent to minimize error. Implement a backpropagation algorithm and identify key components of perceptrons.

        • Introduction to Neural Networks

          Explain essential concepts in neural networks and design neural network architectures. Distinguish between problems based on the objective of the model. Implement appropriate architectures for model objectives

          • Training Neural Networks

            Define a loss function and optimization method to train a neural network. Distinguish between overfitting and underfitting, and identify the causes of each. Optimize the training process using early stopping, regularication, dropout, learning rate decay, and momentum. Distinguish between batch and stochastic gradient descent and build a neural network with PyTorch and run data through it. Test and validate a trained network to ensure it generalizes.

            • Course Project: Developing a Handwritten Digits Classifier with PyTorch

              Develop a handwritten digit recognition system in PyTorch. Then, use data preprocessing skills to load data appropriately for use in models. Develop a neural network using PyTorch and write a training loop that trains the model with the loaded data. Lastly, apply advanced training techniques to improve accuracy on the test set.

            All Our Courses Include

            • Real-world projects from industry experts

              With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.

            • Real-time support

              On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.

            • Workspaces

              Validate your understanding of concepts learned by checking the output and quality of your code in real-time.

            • Flexible learning program

              Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.

            Course offerings

            • Class content

              • Real-world projects
              • Project reviews
              • Project feedback from experienced reviewers
            • Student services

              • Student community
              • Real-time support

            Succeed with personalized services.

            We provide services customized for your needs at every step of your learning journey to ensure your success.

            Get timely feedback on your projects.

            • Personalized feedback
            • Unlimited submissions and feedback loops
            • Practical tips and industry best practices
            • Additional suggested resources to improve
            • 1,400+

              project reviewers

            • 2.7M

              projects reviewed

            • 88/100

              reviewer rating

            • 1.1 hours

              avg project review turnaround time

            Learn with the best.

            Learn with the best.

            • Erick Galinkin

              Principal AI Researcher | Rapid7

              Erick Galinkin is a hacker and computer scientist, leading research at the intersection of security and artificial intelligence at Rapid7. He has spoken at numerous industry and academic conferences on topics ranging from malware development to game theory in security.

            Introduction to Deep Learning

            Get started today

              • Learn

                Dive deep into the fundamental theoretical and practical topics related to deep learning.

              • Average Time

                On average, successful students take 1 month to complete this program.

              • Benefits include

                • Real-world projects from industry experts
                • Real-time support

              Program Details

              • Do I need to apply? What are the admission criteria?

                No. This Course accepts all applicants regardless of experience and specific background.

              • What are the prerequisites for enrollment?

                To be successful in this program learners should have basic knowledge of SQL and relational databases. Additionally, they should have a good understanding of ETL.

              • How is this course structured?

                This course is comprised of content and curriculum to support one project. We estimate that students can complete the program in one month.

                The project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.

              • How long is this course?

                Access to this course runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our programs.

              • Can I switch my start date? Can I get a refund?

                Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.

              • What software and versions will I need in this course?

                There are no software and version requirements to complete this course. All coursework and projects can be completed via Student Workspaces in the Udacity online classroom. Udacity’s full technical requirements are listed here.

              Introduction to Deep Learning

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