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

Jerry Kurata

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.

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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.

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What's inside

Syllabus

Course Overview
Why Learn TensorFlow?
Setting up the TensorFlow Environment
AI and Machine Learning Concepts
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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

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Activities

Coming soon We're preparing activities for Build, Train, and Deploy Your First Neural Network with TensorFlow 2. These are activities you can do either before, during, or after a course.

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.
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.
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.
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.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Build, Train, and Deploy Your First Neural Network with TensorFlow 2.
Comprehensive guide to deep learning, covering the latest techniques and algorithms. It valuable resource for anyone who wants to learn more about deep learning.
Practical guide to machine learning with TensorFlow 2.0, covering a wide range of topics, from data preprocessing to model selection and evaluation. It useful reference for anyone who wants to learn more about machine learning with TensorFlow 2.0.
Comprehensive guide to machine learning with Python, covering a wide range of topics, from data preprocessing to model selection and evaluation. It useful reference for anyone who wants to learn more about machine learning with Python.
Practical guide to deep learning with Python, covering a wide range of topics, from basic concepts to advanced techniques. It useful reference for anyone who wants to learn more about deep learning with Python.
Practical guide to TensorFlow for deep learning, covering a wide range of topics, from basic concepts to advanced techniques. It useful reference for anyone who wants to learn more about TensorFlow for deep learning.
Comprehensive guide to machine learning with C#, covering a wide range of topics, from data preprocessing to model selection and evaluation. It useful reference for anyone who wants to learn more about machine learning with C#.
Practical guide to deep learning with JavaScript, covering a wide range of topics, from basic concepts to advanced techniques. It useful reference for anyone who wants to learn more about deep learning with JavaScript.
Comprehensive guide to machine learning with Go, covering a wide range of topics, from data preprocessing to model selection and evaluation. It useful reference for anyone who wants to learn more about machine learning with Go.

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