Build, Train, and Deploy Your First Neural Network with TensorFlow 2 from Pluralsight

In this course, you will learn the basic principles of machine learning and neural networks so you can quickly create, train, and deploy a neural network with TensorFlow.

TensorFlow is an open source machine learning framework that brings the power of machine learning to everyone. TensorFlow makes it easy for developers to create neural network based machine learning models. In this course, Build, Train, and Deploy Your First Neural Network with TensorFlow 2, you will learn the foundational knowledge needed to create your own neural networks. First, you will explore the basic principles of how machine learning lets us create models that learn from data. Next, you will discover how to apply these principles to neural networks and create a model that predicts the class of clothing in an image. Then, you will delve into how TensorFlow makes it easy to evaluate and improve the performance of neural networks with built-in tools like TensorBoard. Finally, you will learn how to deploy your neural network and make its predictive power available to client applications. When you are finished with this course, you will have the skills and knowledge of machine learning and TensorFlow needed to create, train, and deploy a predictive neural network.

What's inside

Syllabus

Course Overview

Why Learn TensorFlow?

Setting up the TensorFlow Environment

AI and Machine Learning Concepts

Applying the Machine Learning Workflow with TensorFlow

Understanding Neural Networks

Building and Training Your First Neural Network

Monitoring and Improving Neural Network Performance

Deploying Your Neural Network

Final Words

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Promotes the latest version of a popular industry tool, which is TensorFlow 2

Applies these principles to the context of images, which is a core machine learning application

Leverages built-in tools like TensorBoard, which allows learners to easily monitor and improve the performance of their neural networks

Provides step-by-step instructions to help learners deploy their neural networks, making its predictive power available for practical applications

Taught by instructors with expertise and recognition in machine learning and neural networks

Provides a strong foundation for beginners in machine learning and neural networks

Uses a practical approach with hands-on exercises that involve building, training, and deploying neural networks

Emphasizes the importance of AI and machine learning concepts, which are essential for understanding the foundations of neural networks

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 Build, Train, and Deploy Your First Neural Network with TensorFlow 2 with these activities:

Read Deep Learning with Python

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This book provides a comprehensive introduction to deep learning and TensorFlow, which will help you build a strong foundation for this course.

View Deep Learning with Python, Second Edition on Amazon

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Read the book from cover to cover
Complete the exercises in the book
Build a small neural network project using the techniques you learn in the book

Build neural networks with Tensorflow 2

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Follow along with guided tutorials to build neural networks with TensorFlow 2, which will help you apply the concepts you learn in the course.

Browse courses on TensorFlow

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Find a tutorial on building neural networks with TensorFlow 2
Follow the steps in the tutorial to create a neural network model
Train and evaluate your neural network model

Complete Tensorflow exercises

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Practice implementing and training neural networks with Tensorflow to reinforce your understanding of the concepts.

Browse courses on Neural Networks

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Set up your TensorFlow environment
Work through the exercises in the TensorFlow tutorial
Create your own neural network model and train it on a dataset

Four other activities

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Lead a study group for TensorFlow

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Leading a study group will allow you to reinforce your understanding of TensorFlow concepts by teaching them to others and answering their questions.

Browse courses on TensorFlow

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Gather a group of students who are interested in TensorFlow
Choose a topic to cover each week
Prepare materials and activities for each session
Lead the study group sessions

Write a blog post about neural networks

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Writing a blog post about neural networks will force you to think deeply about how they work and how to explain them to others.

Browse courses on Neural Networks

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Choose a specific topic related to neural networks to write about
Research your topic and gather information
Write your blog post
Publish and share your blog post

Develop a machine learning application using TensorFlow

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Building a machine learning application will allow you to apply your TensorFlow skills to a practical project and gain experience in the full machine learning development lifecycle.

Browse courses on TensorFlow

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Identify a problem that you want to solve with machine learning
Gather the necessary data
Build and train a neural network model
Deploy your model and make it available to users

Participate in a Kaggle competition

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Participating in a Kaggle competition will challenge you to apply your TensorFlow skills to real-world problems and improve your ability to solve complex machine learning tasks.

Browse courses on Kaggle Competition

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Find a Kaggle competition that interests you
Download the dataset and familiarize yourself with the problem
Build and train a neural network model
Submit your model to the competition

Career center

Learners who complete Build, Train, and Deploy Your First Neural Network with TensorFlow 2 will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. This course can help Machine Learning Engineers build a strong foundation in the fundamentals of machine learning and neural networks. This knowledge is essential for developing and deploying successful machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use data to solve business problems. This course can help Data Scientists build a strong foundation in the principles of machine learning and neural networks. This knowledge can be applied to a variety of tasks, such as developing predictive models, identifying fraud, and optimizing marketing campaigns.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course, Build, Train, and Deploy Your First Neural Network with TensorFlow 2, can be a valuable tool for Data Analysts, as it provides a solid foundation in the principles of machine learning and neural networks. This knowledge can be applied to a variety of tasks, such as predicting customer behavior, identifying fraud, and optimizing marketing campaigns.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. This course can help Software Engineers build a strong foundation in the principles of machine learning and neural networks. This knowledge can be applied to a variety of tasks, such as developing machine learning-based applications, optimizing software performance, and identifying security vulnerabilities.

See salaries and explore the career path for Software Engineer