Getting Started with Tensorflow 2.0 from Pluralsight

This course focuses on introducing the TensorFlow 2.0 framework - exploring the features and functionality that it offers for building and training neural networks. This course discusses how TensorFlow 2.0 differs from TensorFlow 1.x and how the use of the Keras high-level API and eager execution makes TensorFlow 2.0 a very easy to work with even for complex models.

TensorFlow has long been a powerful and widely used framework for building and training neural network models. In recent years though other frameworks such as PyTorch have gained popularity specifically due to their intuitive programming model which uses dynamic execution graphs. Now TensorFlow 2.0 offers all the ease of use of other frameworks along with TensorFlow's performance and functionality. TensorFlow's use of the Keras high-level API makes designing and training neural networks very straightforward while eager execution makes prototyping and debugging models simple.

First, you will explore the basic features in TensorFlow 2.0 and how its programming model differs from TensorFlow 1.x versions. You will understand the basic working of a neural network and its active learning unit, the neuron.

Next, you will compare and contrast static and dynamic computation graphs and understand the advantages and disadvantages of working with each kind of graph. You will get hands-on exploring execution in TensorFlow 2.0 in eager execution mode and harness the performance efficiencies of static graphs by using the tf.function decorator to decorate ordinary Python functions.

You will then learn how a neural network is trained using gradient descent optimization and how the GradientTape() library in TensorFlow calculates gradients automatically during the training phase of your neural network model.

Finally, you will learn how different APIs in Keras lend themselves to different use-cases. Sequential models consisting of layers stacked one on top of the other are simple and have long been supported by Keras. You will also explore the Functional API and model subclassing in Keras and then use these APIs to build regression as well as classification models

When you’re finished with this course, you will have the skills and knowledge to harness the computational power of the TensorFlow 2.0 framework and choose between the different model-building strategies available in Keras.

What's inside

Syllabus

Course Overview

Exploring the TensorFlow 2.0 Framework

Understanding Dynamic and Static Computation Graphs

Computing Gradients for Model Training

Using the Sequential API in Keras

Using the Functional API and Model Subclassing in Keras

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops understanding of TensorFlow's Keras high-level API and eager execution, which are skills for building and training complex models

Taught by Janani Ravi, who are recognized for their work in TensorFlow

Teaches computation graphs, which are standard knowledge for machine learning

Uses Python, one of the most commonly used programming languages in data science

Explores gradient descent optimization, which is fundamental to training neural networks

Uses the Sequential API, Functional API, and model subclassing in Keras, which are useful for building 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 Getting Started with Tensorflow 2.0 with these activities:

Follow TensorFlow tutorials

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Follow TensorFlow tutorials to supplement your learning and gain practical experience with the framework.

Browse courses on TensorFlow

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Explore the official TensorFlowtutorials.
Find tutorials for specific topics or use cases that interest you.
Follow the tutorials step-by-step and implement the examples.

Practice TensorFlow exercises

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Practice TensorFlow exercises to reinforce your understanding of the framework's functionality and improve your coding skills.

Browse courses on TensorFlow

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Create a new TensorFlow project.
Implement a simple neural network model using TensorFlow's layers API.
Train and evaluate the model on a small dataset.

Build a TensorFlow project

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Create a TensorFlow project to apply your knowledge and develop practical skills in building and deploying machine learning models.

Browse courses on TensorFlow

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Identify a problem or use case that you want to solve with TensorFlow.
Gather data and prepare it for TensorFlow.
Design and implement a TensorFlow model to solve the problem.
Train and evaluate the model.
Deploy the model and monitor its performance.

Show all three activities

Career center

Learners who complete Getting Started with Tensorflow 2.0 will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, build, and deploy machine learning models to solve real-world problems. This course is a great starting point for those looking to enter this field, as it provides a comprehensive overview of the TensorFlow 2.0 framework, which is one of the most popular tools used by Machine Learning Engineers. The course also covers important concepts such as dynamic and static computation graphs, gradient descent optimization, and the different APIs in Keras. With this knowledge, learners will be well-equipped to build and train their own 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 be a valuable asset for aspiring Data Scientists, as it provides a strong foundation in TensorFlow 2.0, which is a powerful tool for data analysis and modeling. The course also covers topics such as gradient descent optimization and the different APIs in Keras, which are essential for building and training machine learning models. With this knowledge, learners will be able to use TensorFlow 2.0 to extract insights from data and solve business problems.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can be a valuable asset for aspiring Quantitative Analysts, as it provides a strong foundation in TensorFlow 2.0, which is a powerful tool for data analysis and modeling. The course also covers topics such as gradient descent optimization and the different APIs in Keras, which are essential for building and training machine learning models. With this knowledge, learners will be able to use TensorFlow 2.0 to analyze financial data and make informed investment decisions.

See salaries and explore the career path for Quantitative Analyst

Data Analyst

Data Analysts clean, analyze, and interpret data. This course can be a valuable asset for aspiring Data Analysts, as it provides a strong foundation in TensorFlow 2.0, which is a powerful tool for data analysis and modeling. The course also covers topics such as gradient descent optimization and the different APIs in Keras, which are essential for building and training machine learning models. With this knowledge, learners will be able to use TensorFlow 2.0 to analyze data and extract insights.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians collect, analyze, and interpret data. This course can be a valuable asset for aspiring Statisticians, as it provides a strong foundation in TensorFlow 2.0, which is a powerful tool for data analysis and modeling. The course also covers topics such as gradient descent optimization and the different APIs in Keras, which are essential for building and training machine learning models. With this knowledge, learners will be able to use TensorFlow 2.0 to analyze data and make informed decisions.

See salaries and explore the career path for Statistician

Financial Analyst

Financial Analysts use data to make investment decisions. This course can be a valuable asset for aspiring Financial Analysts, as it provides a strong foundation in TensorFlow 2.0, which is a powerful tool for data analysis and modeling. The course also covers topics such as gradient descent optimization and the different APIs in Keras, which are essential for building and training machine learning models. With this knowledge, learners will be able to use TensorFlow 2.0 to analyze financial data and make informed investment decisions.

See salaries and explore the career path for Financial Analyst

Business Analyst

Business Analysts use data to solve business problems. This course can be a valuable asset for aspiring Business Analysts, as it provides a strong foundation in TensorFlow 2.0, which is a powerful tool for data analysis and modeling. The course also covers topics such as gradient descent optimization and the different APIs in Keras, which are essential for building and training machine learning models. With this knowledge, learners will be able to use TensorFlow 2.0 to analyze data and make informed business decisions.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be helpful for Software Engineers who want to learn more about machine learning, as it provides a comprehensive overview of the TensorFlow 2.0 framework. The course also covers important concepts such as dynamic and static computation graphs, gradient descent optimization, and the different APIs in Keras. With this knowledge, Software Engineers will be able to incorporate machine learning into their software systems.

See salaries and explore the career path for Software Engineer

Risk Analyst

Risk Analysts use data to assess risk. This course can be a valuable asset for aspiring Risk Analysts, as it provides a strong foundation in TensorFlow 2.0, which is a powerful tool for data analysis and modeling. The course also covers topics such as gradient descent optimization and the different APIs in Keras, which are essential for building and training machine learning models. With this knowledge, learners will be able to use TensorFlow 2.0 to analyze data and assess risk.

See salaries and explore the career path for Risk Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. This course can be a valuable asset for aspiring Operations Research Analysts, as it provides a strong foundation in TensorFlow 2.0, which is a powerful tool for data analysis and modeling. The course also covers topics such as gradient descent optimization and the different APIs in Keras, which are essential for building and training machine learning models. With this knowledge, learners will be able to use TensorFlow 2.0 to analyze data and solve business problems.

See salaries and explore the career path for Operations Research Analyst

Actuary

Actuaries use mathematics and statistics to assess risk. This course can be a valuable asset for aspiring Actuaries, as it provides a strong foundation in TensorFlow 2.0, which is a powerful tool for data analysis and modeling. The course also covers topics such as gradient descent optimization and the different APIs in Keras, which are essential for building and training machine learning models. With this knowledge, learners will be able to use TensorFlow 2.0 to analyze data and assess risk.

See salaries and explore the career path for Actuary

Data Engineer

Data Engineers design, build, and maintain data systems. This course may be helpful for Data Engineers who want to learn more about machine learning, as it provides a comprehensive overview of the TensorFlow 2.0 framework. The course also covers important concepts such as dynamic and static computation graphs, gradient descent optimization, and the different APIs in Keras. With this knowledge, Data Engineers will be able to incorporate machine learning into their data systems.

See salaries and explore the career path for Data Engineer

Software Developer

Software Developers design, develop, and maintain software systems. This course may be helpful for Software Developers who want to learn more about machine learning, as it provides a comprehensive overview of the TensorFlow 2.0 framework. The course also covers important concepts such as dynamic and static computation graphs, gradient descent optimization, and the different APIs in Keras. With this knowledge, Software Developers will be able to incorporate machine learning into their software systems.

See salaries and explore the career path for Software Developer

Computer Vision Engineer

Computer Vision Engineers design and develop systems that can see and interpret images. This course can be a valuable asset for aspiring Computer Vision Engineers, as it provides a strong foundation in TensorFlow 2.0, which is one of the most popular tools used by Computer Vision Engineers. The course also covers important concepts such as dynamic and static computation graphs, gradient descent optimization, and the different APIs in Keras. With this knowledge, learners will be able to use TensorFlow 2.0 to develop computer vision systems that can see and interpret images.

See salaries and explore the career path for Computer Vision Engineer

Machine Learning Researcher

Machine Learning Researchers develop new machine learning algorithms and techniques. This course can be a valuable asset for aspiring Machine Learning Researchers, as it provides a strong foundation in TensorFlow 2.0, which is one of the most popular tools used by Machine Learning Researchers. The course also covers important concepts such as dynamic and static computation graphs, gradient descent optimization, and the different APIs in Keras. With this knowledge, learners will be able to use TensorFlow 2.0 to develop new machine learning algorithms and techniques.

See salaries and explore the career path for Machine Learning Researcher