Deep Learning with Keras 2 from Pluralsight

Deep Learning lies at the heart of many leading machine learning and artificial intelligence applications. This course, Deep Learning with Keras, shows you how to use Keras to quickly create powerful deep neural networks.

There has been a revolution in artificial intelligence (AI) and machine learning, and deep learning-based solutions are leading the charge. Implementing these solutions can be tedious to create and require you to write many lines of complex code. Keras is a library that makes it much easier for you to create these deep learning solutions. In a few lines of code, you can create a model that could require hundreds of lines of conventional code.

This course, Deep Learning with Keras, will get you up to speed with both the theory and practice of using Keras to implement deep neural networks.

First, you will dive deep into learning how Keras implements various layers of neurons quickly and easily, with each layer defining the specific functionality needed to implement parts of your solution.

Next, you will discover how to use Keras’ various methods for interconnecting these layers to form the structure of your deep neural networks. Finally, you will learn how you use Keras to implement several state-of-the-art neural networks, such as the widely used Convolutional and Recurrent Neural Networks, to make these concepts come to life.

By the end of this course, you will gain the skills and experience required to effectively create deep neural networks through the course’s combination of lecture and hands-on coding.

What's inside

Syllabus

Course Overview

Introducing Keras

Creating Your First Neural Network with Keras

Constructing Models in Keras

Employing Layers in Keras Models

Building Convolutional NN with Keras

Implementing Recurrent Neural Nets with Keras

Using Specialty Layers and Functions

Last Words

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops foundational skills in building and implementing Deep Neural Networks with Keras

Teaches practical skills in Deep Learning with Keras

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 Deep Learning with Keras 2 with these activities:

Attend a Deep Learning Meetup

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Attending a deep learning meetup will help you connect with other people who are interested in deep learning and share your knowledge with them.

Browse courses on Deep Learning

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Find a deep learning meetup in your area.
Attend the meetup.
Talk to other people at the meetup.

Volunteer for a Deep Learning Project

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Volunteering for a deep learning project will help you gain practical experience and make a contribution to the field.

Browse courses on Deep Learning

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Find a deep learning project that you are interested in.
Contact the project leader and express your interest in volunteering.
Complete the volunteer training.
Start volunteering on the project.

Dive Deeper into Deep Learning

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Reviewing a book on deep learning will help you gain a deeper understanding of the subject.

View Deep Learning on Amazon

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Choose a book on deep learning that is appropriate for your level of knowledge.
Read the book cover-to-cover, taking notes as you go.
Complete any exercises or assignments that come with the book.

Five other activities

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Follow Keras Tutorials

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Following Keras tutorials will help you learn how to use the Keras library effectively.

Browse courses on Keras

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Find a tutorial that is appropriate for your level of knowledge.
Follow the tutorial step-by-step.
Complete any exercises or assignments that come with the tutorial.

Participate in a Deep Learning Workshop

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Participating in a deep learning workshop will help you learn about new techniques and tools.

Browse courses on Deep Learning

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Find a deep learning workshop that is appropriate for your level of knowledge.
Attend the workshop.
Participate in the workshop activities.

Practice Coding Algorithms

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Practicing coding algorithms will help you solidify your understanding of the fundamental concepts of deep learning.

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Find a website or textbook with coding problems.
Choose a problem and start coding.
Debug your code and get it to work.
Repeat steps 2-3 for as many problems as you have time for.

Create a Blog Post About Deep Learning

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Creating a blog post about deep learning will help you solidify your understanding of the subject and share your knowledge with others.

Browse courses on Deep Learning

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Choose a topic that you want to write about.
Research the topic thoroughly.
Write a draft of your blog post.
Edit and revise your blog post.
Publish your blog post.

Build a Convolutional Neural Network

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Building a convolutional neural network will help you apply your knowledge of deep learning to a real-world problem.

Browse courses on Convolutional Neural Networks

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Choose a problem that you want to solve with a convolutional neural network.
Gather the data that you need to train your network.
Build the network architecture.
Train the network.
Evaluate the network's performance.

Career center

Learners who complete Deep Learning with Keras 2 will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

Deep Learning Engineers design and develop deep learning models. They use these models to solve complex problems in computer vision, natural language processing, and other fields. Deep Learning Engineers need a strong foundation in machine learning, and they need to be able to use Keras and other deep learning frameworks. This course will help you build the skills you need to be a successful Deep Learning Engineer.

See salaries and explore the career path for Deep Learning Engineer

Artificial Intelligence Engineer

Artificial Intelligence Engineers design and develop artificial intelligence systems. These systems can be used to solve a wide range of problems, including image recognition, natural language processing, and speech recognition. Artificial Intelligence Engineers need a strong foundation in machine learning, and they need to be able to use Keras and other deep learning frameworks. This course will help you build the skills you need to be a successful Artificial Intelligence Engineer.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

A Machine Learning Engineer uses data science and machine learning to solve real-world problems. Machine Learning Engineers develop and implement machine learning models, which are used to make predictions and decisions based on data. This course will help you build a strong foundation in machine learning, and it will teach you how to use Keras to create powerful deep neural networks. This knowledge and these skills will be essential for success as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use data to solve business problems. They use machine learning and other techniques to analyze data, identify trends, and make predictions. This course will help you build a strong foundation in machine learning, and it will teach you how to use Keras to create powerful deep neural networks. This knowledge and these skills will be essential for success as a Data Scientist.

See salaries and explore the career path for Data Scientist

Computer Vision Engineer

Computer Vision Engineers design and develop computer vision systems. These systems can be used to solve a wide range of problems, including object detection, facial recognition, and medical imaging. Computer Vision Engineers need a strong foundation in machine learning, and they need to be able to use Keras and other deep learning frameworks. This course will help you build the skills you need to be a successful Computer Vision Engineer.

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Natural Language Processing Engineer

Natural Language Processing Engineers design and develop natural language processing systems. These systems can be used to solve a wide range of problems, including machine translation, text summarization, and sentiment analysis. Natural Language Processing Engineers need a strong foundation in machine learning, and they need to be able to use Keras and other deep learning frameworks. This course will help you build the skills you need to be a successful Natural Language Processing Engineer.

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Speech Recognition Engineer

Speech Recognition Engineers design and develop speech recognition systems. These systems can be used to solve a wide range of problems, including voice control, dictation, and customer service. Speech Recognition Engineers need a strong foundation in machine learning, and they need to be able to use Keras and other deep learning frameworks. This course will help you build the skills you need to be a successful Speech Recognition Engineer.

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Software Engineer

Software Engineers design, develop, and maintain software systems. They use a variety of programming languages and technologies to create software that meets the needs of users. Software Engineers need a strong foundation in computer science, and they need to be able to learn new technologies quickly. This course will help you build the skills you need to be a successful Software Engineer.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts use data to solve business problems. They use a variety of techniques to analyze data, identify trends, and make predictions. Data Analysts need a strong foundation in statistics and mathematics, and they need to be able to communicate their findings effectively. This course may help you build the skills you need to be a successful Data Analyst.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data to solve business problems. They work with stakeholders to understand their needs and then develop solutions that meet those needs. Business Analysts need a strong foundation in business and technology, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Business Analyst.

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Operations Manager

Operations Managers plan and execute operations. They work with stakeholders to define the operations strategy, develop an operations plan, and track progress. Operations Managers need a strong foundation in operations management, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Operations Manager.

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Product Manager

Product Managers develop and manage products. They work with stakeholders to define the product vision, develop a product roadmap, and track progress. Product Managers need a strong foundation in product management, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Product Manager.

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Marketing Manager

Marketing Managers plan and execute marketing campaigns. They work with stakeholders to define the marketing strategy, develop a marketing plan, and track progress. Marketing Managers need a strong foundation in marketing, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Marketing Manager.

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Sales Manager

Sales Managers plan and execute sales campaigns. They work with stakeholders to define the sales strategy, develop a sales plan, and track progress. Sales Managers need a strong foundation in sales, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Sales Manager.

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Project Manager

Project Managers plan and execute projects. They work with stakeholders to define the project scope, develop a project plan, and track progress. Project Managers need a strong foundation in project management, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Project Manager.

See salaries and explore the career path for Project Manager