Literacy Essentials: Core Concepts Deep Learning from Pluralsight

Deep Learning is an essential skill for a data scientist these days. This course will teach you how neural networks work, where they’re used, and how you can apply them to your own problems.

The availability and rise in quality of data, coupled with powerful hardware, has caused massive interest in the field of deep learning. It has become essential for a data scientist or machine learning engineer.

In this course, Literacy Essentials: Core Concepts Deep Learning, you’ll understand and appreciate the inner workings of deep learning and learn how to apply it to a problem.

First, you’ll get an overview of what deep learning is, what it is used for in the industry, and how it differs from machine learning.

Next, you’ll dive deeper into the world of deep learning and understand how the whole operation works.

Finally, you’ll have a quick look at the different types of neural networks and the problems they solve.

When you’re finished with this course, you’ll have a firm understanding of deep learning and the ability to tackle basic problems with it.

What's inside

Syllabus

Course Overview

Introduction to Deep Learning

The Inner Workings of Deep Learning

Types of Neural Networks and Their Uses

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a solid foundation in basic deep learning concepts

The course content aligns with industry demands

Suitable for beginners who seek to gain a comprehensive overview

Teaches fundamental principles through practical applications

Guides learners through the inner workings of deep learning

Requires some prior knowledge or interest in data science

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 Literacy Essentials: Core Concepts Deep Learning with these activities:

Review Linear Algebra and Calculus

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Refreshes your knowledge of mathematical concepts essential for deep learning.

Browse courses on Linear Algebra

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Review the fundamentals of linear algebra, including vectors, matrices, and linear transformations.
Refamiliarize yourself with the concepts of calculus, such as derivatives, integrals, and optimization.
Apply these concepts to solve simple problems related to deep learning.

Read 'Deep Learning' by Goodfellow, Bengio, and Courville

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Provides a comprehensive understanding of the fundamental concepts of deep learning, preparing you for the course.

View Deep Learning on Amazon

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Read the introduction and first chapter to get an overview of deep learning and its applications.
Study the following chapters in order, completing the exercises and understanding the key concepts.
Summarize the main ideas from each chapter in your own words.

Organize and review course materials

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Helps you stay organized and reinforces your understanding of the course content.

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Gather all lecture notes, assignments, quizzes, and exams.
Organize the materials into a logical structure, such as by topic or week.
Review the materials regularly to refresh your memory and identify areas for improvement.

Five other activities

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Join a Deep Learning study group

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Provides a collaborative environment for discussing concepts, sharing insights, and learning from others.

Browse courses on Deep Learning

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Find or create a study group with other students in the course.
Meet regularly to discuss the course material, solve problems, and prepare for assessments.
Take turns presenting topics and leading discussions.

Seek guidance from experts in the field

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Provides access to valuable insights and support from experienced practitioners.

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Identify potential mentors in the field of deep learning, such as professors, researchers, or industry professionals.
Reach out to them and introduce yourself, explaining your interest in deep learning and your desire for guidance.
Meet with your mentors regularly to discuss your progress, ask questions, and get feedback.

Complete the 'Deep Learning Specialization' on Coursera

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Offers a structured and interactive learning experience, reinforcing the course concepts.

Browse courses on Deep Learning

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Sign up for the specialization and complete the introductory module.
Follow along with the video lectures, completing the quizzes and assignments.
Join the discussion forums to ask questions and collaborate with peers.
Complete the capstone project to apply your knowledge to a real-world problem.

Solve Deep Learning problems on LeetCode

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Strengthens your problem-solving skills and deepens your understanding of deep learning concepts.

Browse courses on Deep Learning

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Create an account on LeetCode and search for deep learning problems.
Attempt to solve the problems using the techniques learned in the course.
Review the solutions and explanations to identify areas for improvement.

Build a Deep Learning project

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Provides hands-on experience applying deep learning to a practical problem, reinforcing your learning.

Browse courses on Deep Learning

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Identify a problem that can be solved using deep learning.
Gather the necessary data and prepare it for training.
Choose a suitable deep learning architecture and train a model.
Evaluate the performance of the model and make adjustments as needed.
Deploy the model and monitor its performance in the real world.

Career center

Learners who complete Literacy Essentials: Core Concepts Deep Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data scientists are professionals who use their knowledge of statistics, mathematics, and computer science to analyze data. It's possible to work as a general data scientist, or specialize in specific industries such as healthcare or finance. This course can help you apply your deep learning knowledge and neural networks to your own projects, which can give you a competitive edge in the field and allow you to solve complex problems.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine learning engineers design, develop, and deploy machine learning models. They work with data scientists to understand business problems and then build models that can solve those problems.

See salaries and explore the career path for Machine Learning Engineer

Research Scientist

Research scientists conduct research in a variety of fields, including computer science, mathematics, and engineering. Additionally, they may work in other sectors which make use of scientific methods, such as finance or healthcare. This course may be useful in developing a strong foundation in deep learning concepts, which can be applied to a variety of fields and problems in cutting-edge research.

See salaries and explore the career path for Research Scientist

Software Engineer

Software engineers design, develop, and maintain software applications. This course may be useful in understanding how to apply deep learning and neural networks to software applications, which is a growing trend in the tech industry.

See salaries and explore the career path for Software Engineer

Data Analyst

Data analysts collect, clean, and analyze data to identify trends and patterns. This course may be useful in building a foundation in deep learning concepts, which can be applied to data analysis problems.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial analysts provide investment advice to individuals and organizations. This course may be useful in building a foundation in deep learning concepts, which can be applied to financial analysis problems.

See salaries and explore the career path for Financial Analyst

Product Manager

Product managers develop and manage products. This course may be useful in understanding how to apply deep learning and neural networks to product development problems.

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Consultant

Consultants provide advice to organizations on a variety of topics, including business strategy, operations, and technology. This course may be useful in understanding how to apply deep learning and neural networks to consulting problems.

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Quantitative Analyst

Quantitative analysts use mathematical and statistical models to analyze financial data. They may work in the banking, insurance, or asset management industries.

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Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course may be useful in building a foundation in deep learning concepts, which can be applied to actuarial problems.

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Entrepreneur

Entrepreneurs start and run their own businesses. This course may be useful in understanding how to apply deep learning and neural networks to business problems.

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Market Researcher

Market researchers conduct research to understand consumer behavior and trends. This course may be useful in understanding how to apply deep learning and neural networks to market research problems.

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Operations Research Analyst

Operations research analysts use mathematical and analytical techniques to solve complex problems in a variety of industries, including manufacturing, transportation, and healthcare.

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Business Analyst

Business analysts help organizations understand their business needs and develop solutions to improve performance. They use data and analytics to identify problems and opportunities.

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Statistician

Statisticians collect, analyze, and interpret data. This course can help you apply deep learning and neural networks to statistical problems.

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