Building Deep Learning Models Using Apache MXNet from Pluralsight

Apache MXNet is the deep learning framework which has its origins at Amazon Web Services (AWS) and is a powerful alternative to TensorFlow. This course teaches you how to build dynamic and static computation graphs using the Gluon API.

Apache MXNet offers low-level and high-level APIs which is key to efficiently build neural networks. It also allows you to construct static and dynamic graphs in a symbolic manner using the Module API, the Symbol API, or the Gluon API. In this course, Building Deep Learning Models Using Apache MXNet, you'll learn the basic building blocks of building neural networks using NDArrays, the Module API, the Symbol API, as well as the cutting edge Gluon API. First, you'll gain an understanding of the basic architecture of MXNet and how the basic data structure NDArrays work. Next, you'll discover the difference between symbolic and imperative programming and when you would choose to use one over the other. Then, you'll discover the use of optimizers, loss functions, and data iterators in building and executing neural networks. Finally, you'll explore the Gluon API and build a convolutional neural network for image classification and hybridize it in order to execute a static computation graph. By the end of this course, you'll have the confidence to efficiently build and execute neural networks using all of the APIs that Apache MXNet has to offer.

What's inside

Syllabus

Course Overview

Introduction to Apache MXNet

Building Neural Networks Using the Module API

Building Neural Networks Using the Gluon API

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

If you are a newcomer to using The Gluon API, the capabilities of Apache MXNet, or building neural networks, this is a suitable course to initiate your learning in these technologies

Those with introductory experience in building neural networks and using Apache MXNet, but who wish to delve into the Gluon API, may find this course to be helpful

This course is aimed at developers and data scientists who have experience in building neural networks and are familiar with the fundamentals of Apache MXNet

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Building Deep Learning Models Using Apache MXNet with these activities:

Gather course notes

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Prepare for the course by reviewing all materials online

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Collect all materials from the online course platform
Review course syllabus
Create folders to organize resources

Review: Fundamentals of Neural Networks

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Strengthen the foundation for the course by reviewing the core concepts of neural networks

Browse courses on Neural Networks

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Identify and gather relevant resources
Review key concepts, such as network architectures and learning algorithms
Solve practice problems or exercises

Review linear algebra and calculus

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Strengthen your foundation in mathematics to enhance your understanding of the underlying concepts in Apache MXNet.

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Review textbooks or online resources.
Solve practice problems.
Take practice tests.
Brush up on key concepts.

11 other activities

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Tutorial: Introduction to MXNet API

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Review basic concepts of the MXNet API and its different components

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Find a tutorial on the MXNet API
Follow the tutorial step-by-step
Take notes on key concepts and examples

Discussion Forum: Share and Discuss Course Concepts

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Engage with peers to clarify concepts, exchange ideas, and deepen understanding

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Participate in online discussion forums
Ask questions and provide thoughtful responses
Summarize key points and insights

Implement a fully connected neural network in Gluon

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Practice implementing a basic neural network in Gluon to reinforce your understanding of the API.

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Create a new Gluon project.
Import necessary modules.
Define the network architecture.
Initialize the network.
Train the network on a dataset.

Practice exercises: Building neural networks with Gluon API

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Apply the knowledge gained from the course by building neural networks using the Gluon API

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Find practice exercises or assignments
Solve the exercises and build neural networks
Review the solutions and identify areas for improvement

Organize notes and resources from the course

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Ensure easy access to essential information by organizing your course materials.

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Gather all notes, assignments, and quizzes.
Create a system for organizing the materials.
Review and update the materials regularly.
Store the materials in a convenient and accessible location.

Solve coding challenges related to Apache MXNet

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Challenge yourself with coding problems to enhance your problem-solving skills in Apache MXNet.

Browse courses on Apache MXNet

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Find coding challenges online or create your own.
Attempt to solve the challenges.
Review your solutions and identify areas for improvement.
Repeat the process until you are confident in your abilities.

Attend online meetups or conferences related to Apache MXNet

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Connect with professionals in the field to learn about industry trends and best practices related to Apache MXNet.

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Identify relevant meetups or conferences.
Register for the events.
Attend the events and engage with participants.
Follow up with new connections.

Project: Implement a Convolutional Neural Network for Image Classification

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Demonstrate understanding of course concepts by implementing a CNN for image classification

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Gather the necessary data and prepare the dataset
Design and implement the CNN architecture using the Gluon API
Train and evaluate the CNN on the prepared dataset
Document the project and present the results

Contribute to MXNet Open-Source Community

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Gain practical experience and enhance understanding by contributing to the MXNet open-source project

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Familiarize with the MXNet codebase and community guidelines
Identify a project or area to contribute to
Implement changes or improvements
Submit a pull request and engage with the community

Develop a convolutional neural network for image classification

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Build a practical application of the concepts learned by creating a neural network for image classification.

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Choose a dataset.
Prepare the data.
Design and implement the network.
Train and evaluate the network.
Deploy the network.

Project: Develop a Neural Network for a Personal Project

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Apply course knowledge to a real-world project, fostering creativity and problem-solving skills

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Brainstorm and define a personal project idea
Gather and prepare the necessary data
Design and implement the neural network solution
Evaluate the performance and iterate on the solution
Document and present the project outcomes

Career center

Learners who complete Building Deep Learning Models Using Apache MXNet will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

Deep Learning Engineers design, implement, and maintain deep learning models. They work to optimize these models and measure their performance. This course aligns perfectly with the Deep Learning Engineer role. This course, Building Deep Learning Models Using Apache MXNet, teaches learners how to build deep learning models using Apache MXNet's high-level APIs. These are valuable skills for Deep Learning Engineers to have.

See salaries and explore the career path for Deep Learning Engineer

Data Scientist

Data Scientists work to understand complex data sets. They devise new algorithms to solve problems in various industries. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Data Science, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. This is a skill that is not only valuable for Data Science, but for many other technical roles where deep learning is applied.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design and implement machine learning models. These models are applied in a variety of industries to solve complex data problems. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Machine Learning, as this course teaches how to build neural networks using Apache MXNet's high-level APIs.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data analysis to help businesses improve their performance. They work on a variety of projects, from developing new products to improving customer service. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Business Analysis, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. These skills enable Business Analysts to contribute to data-driven decision making.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design, develop, and maintain software. They work on a variety of projects, from small personal projects to large enterprise systems. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Software Engineering, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. Engineers with the ability to build deep learning models are highly sought after.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts collect, clean, and analyze data. They use their findings to help businesses make informed decisions. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Data Analytics, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. This is a valuable skill for Data Analysts to have.

See salaries and explore the career path for Data Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Product Management, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. Building deep learning models is a valuable skill for solving many of the complex problems faced by Product Managers.

See salaries and explore the career path for Product Manager

Project Manager

Project Managers are responsible for planning, executing, and completing projects. They work with a variety of stakeholders to ensure that projects are completed on time, within budget, and to the required quality. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Project Management, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. Knowledge in deep learning can give Project Managers insights into practical applications that benefit their projects.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to assess risk and make investment decisions. They work in a variety of industries, including finance, insurance, and consulting. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Quantitative Analysis, as this course teaches how to build neural networks using Apache MXNet's high-level APIs.

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Technical Writer

Technical Writers create documentation for a variety of products and services. They work with engineers, scientists, and other experts to translate complex technical information into clear and concise language. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Technical Writing, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. This will give the Technical Writer the background knowledge to understand the technical information they are tasked with documenting.

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Teacher

Teachers educate students at all levels, from kindergarten to college. They develop lesson plans, teach classes, and assess student learning. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Teaching, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. This knowledge can extend far beyond teaching technical subjects as well.

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Software Architect

Software Architects design and develop software systems. They work with a variety of stakeholders to ensure that software systems are efficient, reliable, and scalable. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Software Architecture, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. This is a valuable skill for Software Architects to have.

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Research Scientist

Research Scientists perform cutting-edge research in a variety of fields. They may focus on developing new theories, or on applying existing theories to solve real-world problems. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Research, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. Developing theories and solving real-world problems is heavily reliant on understanding how to build deep learning models.

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Sales Engineer

Sales Engineers work with customers to help them understand and purchase complex products and services. They provide technical expertise and support to customers throughout the sales process. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Sales Engineering, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. Sales Engineers with knowledge of deep learning models are highly sought after.

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Consultant

Consultants provide advice and guidance to businesses on a variety of topics. They work with businesses to improve their performance, solve problems, and develop new strategies. This course in Deep Learning using Apache MXNet may be useful for someone entering the field of Consulting, as this course teaches how to build neural networks using Apache MXNet's high-level APIs. Deep learning is highly applicable to solving complex business problems that Consultants are faced with.

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