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Amit Yadav
In this 1.5 hour long project-based course, you will learn about constants and variables in TensorFlow, you will learn how to use automatic differentiation, and you will apply automatic differentiation to solve a linear regression problem. By the end of this project, you will have a good understanding of how machine learning algorithms can be implemented in TensorFlow.
Read more
In this 1.5 hour long project-based course, you will learn about constants and variables in TensorFlow, you will learn how to use automatic differentiation, and you will apply automatic differentiation to solve a linear regression problem. By the end of this project, you will have a good understanding of how machine learning algorithms can be implemented in TensorFlow.
Read more
In this 1.5 hour long project-based course, you will learn about constants and variables in TensorFlow, you will learn how to use automatic differentiation, and you will apply automatic differentiation to solve a linear regression problem. By the end of this project, you will have a good understanding of how machine learning algorithms can be implemented in TensorFlow.
In order to be successful in this project, you should be familiar with Python, Gradient Descent, Linear Regression.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/t1pgxi/regression
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What's inside
Syllabus
Regression with Automatic Differentiation in TensorFlow
Welcome to Regression with Automatic Differentiation in TensorFlow. In this project, we will get started with some of the important basics of TensorFlow - like tensor constants, variables, and automatic differentiation. We will then apply this knowledge to solve a linear regression problem. By the end of the project, you will have a good understanding on how to approach implementing machine learning algorithms in TensorFlow.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops concepts and ideas that are core requirements for success in machine learning algorithms
Teaches machine learning algorithms, which are highly relevant to industry
Takes a project-based approach, which is standard for machine learning learning
Its lessons are hands-on, which is beneficial for understanding machine learning properly
Facilitates interactivity, which is useful for engagement
Applies theories to real-world problems, which reflects the actual use cases for this topic
Save this course
Save Regression with Automatic Differentiation in TensorFlow to your list so you can find it easily later:
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Reviews summary
Tensorflow regression course
According to students, this course provides a hands-on training experience for beginners who want to learn regression with the TensorFlow library. It includes video lectures, assignments, and projects. The course is well-received with learners perceiving its practical examples and engaging assignments as strengths. However, some students noted that a more sophisticated example beyond linear regression would have been helpful.
Engaging Projects
"Thanks for the project!"
"A very basic project for beginners to begin with."
"Great Course."
Potential Technical Issues with VMs
"content is alright, but the VM crashed, so I have only watched the last couple of videos without applying."
Limited Sophisticated Examples
"Would've liked if a more sophisticated example was taken instead of linear regression."
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 Regression with Automatic Differentiation in TensorFlow with these
activities:
Review Python basics
Show steps
Refresh your understanding of Python syntax and data structures before starting the course.
Browse courses on
Python
Show steps
-
Review a Python tutorial or documentation.
-
Practice writing simple Python programs.
Review Gradient Descent
Show steps
Review the concepts of Gradient Descent to build foundational knowledge for the course.
Browse courses on
Gradient Descent
Show steps
-
Revisit the mathematical formulation of Gradient Descent.
-
Study examples of Gradient Descent applied to different functions.
-
Practice implementing Gradient Descent in a programming language.
TensorFlow Tutorial Exploration
Show steps
Review official TensorFlow tutorials to reinforce course concepts and broaden your knowledge of the library. Focus on topics such as advanced tensor operations, model building, and optimization.
Browse courses on
TensorFlow
Show steps
-
Identify relevant TensorFlow tutorials.
-
Work through the tutorials at your own pace.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Review Python
Show steps
Review fundamental Python skills to reinforce foundational knowledge for this ML course
Browse courses on
Python
Show steps
-
Review Variables and Data Types
-
Practice using Control Flow
-
Complete example programming problems
TensorFlow Code Exercises
Show steps
Solve a series of coding exercises that challenge your understanding of TensorFlow fundamentals, such as creating tensors, performing operations, and using control flow.
Browse courses on
TensorFlow
Show steps
-
Set up a TensorFlow development environment.
-
Complete a set of coding exercises on TensorFlow basics.
Create a TensorFlow cheat sheet
Show steps
Summarize the key concepts and operations of TensorFlow in a cheat sheet for quick reference.
Show steps
-
Review the TensorFlow documentation and tutorials.
-
Extract the most important concepts and operations.
-
Organize the information into a clear and concise format.
-
Create the cheat sheet, either as a physical document or a digital file.
TensorFlow Resources
Show steps
Compile resources for further learning and reference
Show steps
-
Collect TensorFlow tutorials
-
Gather TensorFlow documentation
-
Find TensorFlow community forums
TensorFlow Mentor
Show steps
Reach out to a mentor for guidance and support
Show steps
-
Identify a potential mentor
-
Request mentorship
-
Meet with the mentor regularly
Practice TensorFlow operations
Show steps
Solve a series of exercises on the TensorFlow website to reinforce your understanding of constants, variables, and automatic differentiation.
Show steps
-
Visit the TensorFlow website and find the exercises on constants, variables, and automatic differentiation.
-
Solve the exercises, referring to the documentation as needed.
Build a Simple Linear Regression Model
Show steps
Gain practical experience by implementing a simple linear regression model, solidifying the concepts learned in the course.
Browse courses on
Linear Regression
Show steps
-
Gather a dataset with relevant features and target variables.
-
Preprocess the data and prepare it for modeling.
-
Implement the linear regression algorithm using TensorFlow.
-
Evaluate the performance of the model using metrics such as R-squared and mean squared error.
Exploration of Automatic Differentiation
Show steps
Create either a detailed blog article, a video tutorial, or a software package that explores the concepts of automatic differentiation and its implementation in TensorFlow.
Browse courses on
Automatic Differentiation
Show steps
-
Research the fundamental concepts of automatic differentiation.
-
Experiment with the TensorFlow API for automatic differentiation.
-
Apply automatic differentiation to a real-world problem.
Follow a TensorFlow tutorial
Show steps
Follow a tutorial on the TensorFlow website or from a reputable source to learn how to implement a machine learning algorithm in TensorFlow.
Show steps
-
Search for a TensorFlow tutorial that covers a topic of your interest.
-
Follow the tutorial step-by-step, implementing the code in your own environment.
-
Test the code to ensure it works correctly.
TensorFlow Tutorial
Show steps
Follow tutorials to learn about TensorFlow concepts and solidify knowledge gained in the course
Show steps
-
Complete the TensorFlow Tutorial
-
Build a TensorFlow model
-
Debug the model
Linear Regression Implementation
Show steps
Implement a linear regression model from scratch using TensorFlow. This project will reinforce the concepts of constants, variables, and automatic differentiation covered in the course.
Browse courses on
Linear Regression
Show steps
-
Design the architecture of the linear regression model.
-
Implement the forward and backward passes of the model.
-
Train the model on a dataset.
-
Evaluate the performance of the model.
TensorFlow Study Group
Show steps
Engage with fellow learners to clarify concepts
Show steps
-
Join or create a study group
-
Discuss course material
-
Work on problems together
Participate in a TensorFlow competition
Show steps
Apply your TensorFlow skills by participating in a competition, such as the TensorFlow World Modelers Competition.
Show steps
-
Research TensorFlow competitions and find one that aligns with your interests.
-
Form a team or work individually.
-
Develop a solution using TensorFlow.
-
Submit your solution to the competition.
Build a TensorFlow project
Show steps
Apply your TensorFlow knowledge by building a project, such as an image classifier or a natural language processing model.
Show steps
-
Identify a problem that can be solved using TensorFlow.
-
Gather the necessary data and resources.
-
Design and implement a TensorFlow model.
-
Evaluate and improve the performance of the model.
-
Deploy the model to a production environment.
TensorFlow Exercises
Show steps
Practice TensorFlow skills to enhance understanding and improve retention
Show steps
-
Solve TensorFlow exercises
-
Create a TensorFlow project
-
Troubleshoot errors
TensorFlow Workshop
Show steps
Attend a workshop led by an experienced TensorFlow developer
Show steps
-
Register for the workshop
-
Attend the workshop
-
Apply what you learn in the workshop
TensorFlow Project
Show steps
Develop a TensorFlow project to demonstrate understanding and apply skills
Show steps
-
Design the project
-
Create a dataset
-
Train a TensorFlow model
-
Evaluate the model
-
Document the project
Review Python basics
Show steps
Refresh your understanding of Python syntax and data structures before starting the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review a Python tutorial or documentation.
- Practice writing simple Python programs.
Review Gradient Descent
Show steps
Review the concepts of Gradient Descent to build foundational knowledge for the course.
Browse courses on
Gradient Descent
Show steps
Show steps
Show steps
- Revisit the mathematical formulation of Gradient Descent.
- Study examples of Gradient Descent applied to different functions.
- Practice implementing Gradient Descent in a programming language.
TensorFlow Tutorial Exploration
Show steps
Review official TensorFlow tutorials to reinforce course concepts and broaden your knowledge of the library. Focus on topics such as advanced tensor operations, model building, and optimization.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify relevant TensorFlow tutorials.
- Work through the tutorials at your own pace.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Review Python
Show steps
Review fundamental Python skills to reinforce foundational knowledge for this ML course
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Variables and Data Types
- Practice using Control Flow
- Complete example programming problems
TensorFlow Code Exercises
Show steps
Solve a series of coding exercises that challenge your understanding of TensorFlow fundamentals, such as creating tensors, performing operations, and using control flow.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Set up a TensorFlow development environment.
- Complete a set of coding exercises on TensorFlow basics.
Create a TensorFlow cheat sheet
Show steps
Summarize the key concepts and operations of TensorFlow in a cheat sheet for quick reference.
Show steps
Show steps
Show steps
- Review the TensorFlow documentation and tutorials.
- Extract the most important concepts and operations.
- Organize the information into a clear and concise format.
- Create the cheat sheet, either as a physical document or a digital file.
TensorFlow Resources
Show steps
Compile resources for further learning and reference
Show steps
Show steps
Show steps
- Collect TensorFlow tutorials
- Gather TensorFlow documentation
- Find TensorFlow community forums
TensorFlow Mentor
Show steps
Reach out to a mentor for guidance and support
Show steps
Show steps
Show steps
- Identify a potential mentor
- Request mentorship
- Meet with the mentor regularly
Practice TensorFlow operations
Show steps
Solve a series of exercises on the TensorFlow website to reinforce your understanding of constants, variables, and automatic differentiation.
Show steps
Show steps
Show steps
- Visit the TensorFlow website and find the exercises on constants, variables, and automatic differentiation.
- Solve the exercises, referring to the documentation as needed.
Build a Simple Linear Regression Model
Show steps
Gain practical experience by implementing a simple linear regression model, solidifying the concepts learned in the course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Gather a dataset with relevant features and target variables.
- Preprocess the data and prepare it for modeling.
- Implement the linear regression algorithm using TensorFlow.
- Evaluate the performance of the model using metrics such as R-squared and mean squared error.
Exploration of Automatic Differentiation
Show steps
Create either a detailed blog article, a video tutorial, or a software package that explores the concepts of automatic differentiation and its implementation in TensorFlow.
Browse courses on
Automatic Differentiation
Show steps
Show steps
Show steps
- Research the fundamental concepts of automatic differentiation.
- Experiment with the TensorFlow API for automatic differentiation.
- Apply automatic differentiation to a real-world problem.
Follow a TensorFlow tutorial
Show steps
Follow a tutorial on the TensorFlow website or from a reputable source to learn how to implement a machine learning algorithm in TensorFlow.
Show steps
Show steps
Show steps
- Search for a TensorFlow tutorial that covers a topic of your interest.
- Follow the tutorial step-by-step, implementing the code in your own environment.
- Test the code to ensure it works correctly.
TensorFlow Tutorial
Show steps
Follow tutorials to learn about TensorFlow concepts and solidify knowledge gained in the course
Show steps
Show steps
Show steps
- Complete the TensorFlow Tutorial
- Build a TensorFlow model
- Debug the model
Linear Regression Implementation
Show steps
Implement a linear regression model from scratch using TensorFlow. This project will reinforce the concepts of constants, variables, and automatic differentiation covered in the course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Design the architecture of the linear regression model.
- Implement the forward and backward passes of the model.
- Train the model on a dataset.
- Evaluate the performance of the model.
TensorFlow Study Group
Show steps
Engage with fellow learners to clarify concepts
Show steps
Show steps
Show steps
- Join or create a study group
- Discuss course material
- Work on problems together
Participate in a TensorFlow competition
Show steps
Apply your TensorFlow skills by participating in a competition, such as the TensorFlow World Modelers Competition.
Show steps
Show steps
Show steps
- Research TensorFlow competitions and find one that aligns with your interests.
- Form a team or work individually.
- Develop a solution using TensorFlow.
- Submit your solution to the competition.
Build a TensorFlow project
Show steps
Apply your TensorFlow knowledge by building a project, such as an image classifier or a natural language processing model.
Show steps
Show steps
Show steps
- Identify a problem that can be solved using TensorFlow.
- Gather the necessary data and resources.
- Design and implement a TensorFlow model.
- Evaluate and improve the performance of the model.
- Deploy the model to a production environment.
TensorFlow Exercises
Show steps
Practice TensorFlow skills to enhance understanding and improve retention
Show steps
Show steps
Show steps
- Solve TensorFlow exercises
- Create a TensorFlow project
- Troubleshoot errors
TensorFlow Workshop
Show steps
Attend a workshop led by an experienced TensorFlow developer
Show steps
Show steps
Show steps
- Register for the workshop
- Attend the workshop
- Apply what you learn in the workshop
TensorFlow Project
Show steps
Develop a TensorFlow project to demonstrate understanding and apply skills
Show steps
Show steps
Show steps
- Design the project
- Create a dataset
- Train a TensorFlow model
- Evaluate the model
- Document the project
Career center
Learners who complete Regression with Automatic Differentiation in TensorFlow will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and implement machine learning algorithms to solve a variety of problems, including image recognition, natural language processing, and speech recognition. This course provides a strong foundation in the fundamentals of machine learning, including automatic differentiation, which is a key technique used in the development of machine learning algorithms. Taking this course will help you build the skills you need to succeed as a Machine Learning Engineer.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical techniques to analyze data and extract insights. This course provides a strong foundation in the fundamentals of machine learning, including automatic differentiation, which is a key technique used in the development of machine learning algorithms. Taking this course will help you build the skills you need to succeed as a Data Scientist.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course provides a strong foundation in the fundamentals of TensorFlow, a popular machine learning library. Taking this course will help you build the skills you need to develop and maintain machine learning applications.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course provides a strong foundation in the fundamentals of machine learning, including automatic differentiation, which is a key technique used in the development of financial models. Taking this course will help you build the skills you need to succeed as a Quantitative Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course provides a strong foundation in the fundamentals of machine learning, including automatic differentiation, which is a key technique used in the development of optimization models. Taking this course will help you build the skills you need to succeed as an Operations Research Analyst.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course provides a strong foundation in the fundamentals of machine learning, including automatic differentiation, which is a key technique used in the development of actuarial models. Taking this course will help you build the skills you need to succeed as an Actuary.
Statistician
Statistician
Statisticians use mathematical and statistical models to analyze data and extract insights. This course provides a strong foundation in the fundamentals of machine learning, including automatic differentiation, which is a key technique used in the development of statistical models. Taking this course will help you build the skills you need to succeed as a Statistician.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. This course provides a strong foundation in the fundamentals of machine learning, including automatic differentiation, which is a key technique used in the development of data analysis pipelines. Taking this course will help you build the skills you need to succeed as a Data Analyst.
Business Analyst
Business Analyst
Business Analysts use data and analytics to solve business problems. This course provides a strong foundation in the fundamentals of machine learning, including automatic differentiation, which is a key technique used in the development of business analytics solutions. Taking this course will help you build the skills you need to succeed as a Business Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use data and analytics to make investment decisions. This course provides a strong foundation in the fundamentals of machine learning, including automatic differentiation, which is a key technique used in the development of financial models. Taking this course will help you build the skills you need to succeed as a Financial Analyst.
Product Manager
Product Manager
Product Managers develop and manage software products. This course provides a strong foundation in the fundamentals of machine learning, including automatic differentiation, which is a key technique used in the development of machine learning products. Taking this course will help you build the skills you need to succeed as a Product Manager.
Project Manager
Project Manager
Project Managers plan and execute projects. This course may be useful for Project Managers who want to learn more about machine learning and its applications. Taking this course will help you build a foundation in the fundamentals of machine learning, including automatic differentiation.
Technical Writer
Technical Writer
Technical Writers write documentation for software and other technical products. This course may be useful for Technical Writers who want to learn more about machine learning and its applications. Taking this course will help you build a foundation in the fundamentals of machine learning, including automatic differentiation.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns. This course may be useful for Marketing Managers who want to learn more about machine learning and its applications. Taking this course will help you build a foundation in the fundamentals of machine learning, including automatic differentiation.
Sales Manager
Sales Manager
Sales Managers develop and execute sales strategies. This course may be useful for Sales Managers who want to learn more about machine learning and its applications. Taking this course will help you build a foundation in the fundamentals of machine learning, including automatic differentiation.
For more career information including salaries, visit:
OpenCourser.com/course/t1pgxi/regression
Reading list
We've selected nine 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
Regression with Automatic Differentiation in TensorFlow.
Uses case studies and real-world datasets to teach advanced machine learning techniques such as neural networks and deep learning. It's a valuable resource to reference when implementing machine learning concepts in TensorFlow.
Is specifically about using TensorFlow for deep learning. It covers topics such as convolutional neural networks, recurrent neural networks, and natural language processing, which are not covered in the course.
Provides a comprehensive overview of deep learning, with a focus on using Python and the Keras library. It's a good resource for contextualizing the concepts covered in the course.
Focuses on the practical aspects of machine learning in Python. It covers popular libraries such as scikit-learn, TensorFlow, and PyTorch, providing a hands-on approach that complements the theoretical concepts in the course.
Provides a concise overview of applied machine learning, covering foundational concepts, algorithms, and use cases. It complements the course's theoretical underpinnings with a practical perspective.
While this book uses R instead of Python, it provides a comprehensive overview of machine learning principles, algorithms, and techniques. It offers a different perspective on machine learning concepts, broadening the learner's understanding.
Offers a collection of recipes and solutions for common machine learning tasks in TensorFlow. It provides concise, practical guidance for implementing machine learning models, bridging the gap between theory and application.
Provides a concise overview of machine learning algorithms, including linear regression, decision trees, and neural networks. It serves as a good starting point for learners who want to explore different machine learning algorithms and their mathematical foundations.
Provides a comprehensive overview of artificial intelligence, covering topics from search and planning to natural language processing and machine learning. It offers a broad context for the concepts covered in the course and stimulates further exploration of AI.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/t1pgxi/regression
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Effort
1.5 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Develops concepts and ideas that are core requirements for success in machine learning algorithms
Teaches machine learning algorithms, which are highly relevant to industry
Takes a project-based approach, which is standard for machine learning learning
Its lessons are hands-on, which is beneficial for understanding machine learning properly
Facilitates interactivity, which is useful for engagement
Applies theories to real-world problems, which reflects the actual use cases for this topic
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