Understanding the Foundations of TensorFlow from Pluralsight

This course introduces TensorFlow, an open source data flow library for numerical computations using data flow graphs.

In this course, Understanding the Foundations of TensorFlow, you'll learn the TensorFlow library from very first principles. First, you'll start with the basics of machine learning using linear regression as an example and focuses on understanding fundamental concepts in TensorFlow. Next, you'll discover how to apply them to machine learning, the concept of a Tensor, the anatomy of a simple program, basic constructs such as constants, variables, placeholders, sessions, and the computation graph. Then, you'll be introduced to TensorBoard, the visualization tool used to view and debug the data flow graphs. You'll work with basic math operations and image transformations to see how common computations are performed. Finally, you'll solve a real world machine learning problem using the MNIST handwritten dataset and the k-nearest-neighbours algorithm. By the end of this course, you'll have a better understanding of the foundations of TensorFlow.

What's inside

Syllabus

Course Overview

Introducing TensorFlow

Introducing Computation Graphs

Digging Deeper into Fundamentals

Working with Images

Solving Basic Math Functions

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops foundational concepts in TensorFlow, a fundamental library for data flow computations using data flow graphs

Introduces core machine learning concepts while using TensorFlow, suitable for learners in the early stages of their machine learning journey

Delves into practical applications, including image transformations and solving math functions, enhancing comprehension of TensorFlow's capabilities

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 Understanding the Foundations of TensorFlow with these activities:

Review the fundamentals of TensorFlow

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Having a strong foundational understanding of TensorFlow will aid with building a stronger foundation to better prepare for course materials

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Read the official TensorFlow documentation
Go through the TensorFlow tutorials
Complete the TensorFlow Coursera specialization

Work through TensorFlow tutorials

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Working through these tutorials will offer deeper insight and skills development

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Follow the TensorFlow tutorials on the official website
Complete the TensorFlow tutorials on Coursera
Go through the TensorFlow tutorials on YouTube

Solve TensorFlow exercises

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Problem solving reinforces learning and develops valuable skills

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Complete the exercises in the TensorFlow documentation
Solve the TensorFlow exercises on Kaggle
Participate in the TensorFlow community forums

Four other activities

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Join a TensorFlow study group

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Peer interaction and sharing of knowledge enhances learning experiences

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Find a TensorFlow study group online or in your local area
Attend the study group meetings regularly
Participate in discussions and share your knowledge

Write a TensorFlow blog post

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Creating content requires deep understanding and reinforces learning through teaching others

Browse courses on TensorFlow

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Choose a topic related to TensorFlow
Research the topic thoroughly
Write a well-structured blog post
Publish the blog post on a reputable platform

Mentor a junior TensorFlow developer

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Mentoring others reinforces learning through teaching and sharing knowledge

Browse courses on TensorFlow

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Identify a junior TensorFlow developer who needs mentoring
Set up regular mentoring sessions
Share your knowledge and experience
Help the mentee develop their skills

Build a TensorFlow project

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Project based learning enhances skills and knowledge development by applying learning to solve real world problems

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Identify a problem that can be solved using TensorFlow
Design and implement a TensorFlow solution
Test and evaluate the solution
Deploy the solution

Career center

Learners who complete Understanding the Foundations of TensorFlow will develop knowledge and skills that may be useful to these careers:

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment recommendations. This course may be useful as you develop a strong foundation in TensorFlow, which is widely used in the financial industry for applications such as risk management and algorithmic trading.

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Research Scientist

A Research Scientist conducts scientific research to develop new knowledge and technologies. This course may be useful as you explore novel approaches to machine learning and artificial intelligence, and apply your knowledge to real-world problems.

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Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning systems to solve real-world problems. This course may be useful as you build models for supervised and unsupervised learning, and gain hands-on experience with image recognition using TensorFlow.

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Business Intelligence Analyst

A Business Intelligence Analyst collects, analyzes, and interprets data to help businesses improve their performance. This course may be useful as you work with data to identify trends and patterns, and gain experience in using TensorFlow for data visualization and decision-making.

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Data Analyst

A Data Analyst collects, analyzes, and interprets data to help businesses make informed decisions. This course may be useful as you work with data in a variety of formats, and gain experience in using TensorFlow for data analysis and visualization.

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Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical models to solve complex business problems. This course may be useful as you develop a strong foundation in TensorFlow, which is used in a variety of operations research applications, such as optimization and simulation.

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Financial Analyst

A Financial Analyst provides financial advice to individuals and businesses. This course may be useful as you work with financial data and models, and gain experience in using TensorFlow for financial analysis and forecasting.

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Risk Analyst

A Risk Analyst identifies, assesses, and manages risks for businesses and organizations. This course may be useful as you develop a strong foundation in TensorFlow, which is used in a variety of risk management applications, such as fraud detection and credit scoring.

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Statistician

A Statistician collects, analyzes, and interprets data to provide insights and make decisions. This course may be useful as you work with data in a variety of formats, and gain experience in using TensorFlow for statistical analysis and modeling.

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Actuary

An Actuary uses mathematical and statistical models to assess and manage financial risks. This course may be useful as you develop a strong foundation in TensorFlow, which is used in a variety of actuarial applications, such as insurance pricing and risk modeling.

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Data Scientist

A Data Scientist collects, analyzes, and interprets data to extract meaningful insights and solve complex business problems. This course may be useful as you work with TensorBoard, the visualization tool used to view and debug the data flow graphs.

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Data Engineer

A Data Engineer designs, builds, and maintains data pipelines to support data-driven applications. This course may be useful as you work with data in a variety of formats, and gain experience in using TensorFlow for data processing and transformation.

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Software Tester

A Software Tester evaluates software to ensure it meets requirements and performs as expected. This course may be useful as you gain experience in using TensorFlow for testing and debugging machine learning models.

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Software Developer

A Software Developer designs, develops, and implements software using a variety of programming languages. This course may be useful as you work on data-driven applications and processes.

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Database Administrator

A Database Administrator manages and maintains databases. This course may be useful as you gain experience in using TensorFlow for data storage and retrieval.

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