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Building Regression Models Using TensorFlow 1

Vitthal Srinivasan

TensorFlow is the tool of choice for building deep learning applications. In this course, you'll learn how the neurons in neural networks learn non-linear functions, and how neural networks execute operations such as regression and classification.

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TensorFlow is the tool of choice for building deep learning applications. In this course, you'll learn how the neurons in neural networks learn non-linear functions, and how neural networks execute operations such as regression and classification.

TensorFlow is all about building neural networks that can "learn" functions, and linear regression can be learnt by the simplest possible neural network - of just 1 neuron! In contrast, the XOR function requires 3 neurons arranged in 2 layers, and smart image recognition can require thousands of neurons. In this course, Building Regression Models using TensorFlow, you'll learn how the neurons in neural networks learn non-linear functions. First, you'll begin by learning functions such as XOR, and how to train different gradient descent optimizers. Next, you'll dive into the implications of choosing activation functions, such as softmax and ReLU. Finally, you'll explore the use of built-in estimators in Tensorflow. By the end of this course, you'll have a better understanding of how neurons "learn", and how neural networks in TensorFlow are set up and trained to execute operations such as regression and classification.

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What's inside

Syllabus

Course Overview
Learning Using Neurons
Building Linear Regression Models Using TensorFlow
Building Logistic Regression Models Using TensorFlow
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Building Generalized Linear Models Using Estimators

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Useful for understanding non-linear functions and regression in deep learning
In-depth exploration of neural networks' functions and their use in regression models
Taught by Vitthal Srinivasan, an experienced instructor in deep learning
Covers fundamental concepts of TensorFlow, making it suitable for beginners
Emphasizes hands-on practice through building regression models
May require prior knowledge of deep learning concepts and TensorFlow

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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 Building Regression Models Using TensorFlow 1 with these activities:
Read 'Machine Learning with TensorFlow' by Nishant Shukla
This book provides a comprehensive overview of TensorFlow and machine learning concepts, which will complement the course material.
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  • Read the book thoroughly.
  • Take notes and highlight important concepts.
  • Complete the exercises and assignments in the book.
Review Linear Algebra
The course is heavily based on linear algebra concepts, so this activity will help you brush up on these.
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  • Re-read your old notes or textbooks on linear algebra.
  • Go through online tutorials or videos on linear algebra.
  • Solve practice problems on linear algebra.
Review Linear Algebra
Brushing up on your linear algebra skills will help you understand the mathematical foundations of machine learning.
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  • Review your lecture notes from a previous linear algebra course.
  • Solve practice problems from a linear algebra textbook or online resource.
  • Take a refresher course or watch video tutorials on linear algebra.
11 other activities
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Join a Study Group
Joining a study group can help you stay motivated, learn from others, and improve your understanding of the course material.
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  • Find a study group or create your own.
  • Meet regularly to discuss the course material.
  • Work together on projects and assignments.
  • Quiz each other on the concepts you're learning.
Join a TensorFlow Learning Group
The course is challenging and having a peer support group will aid your understanding of the concepts covered in the course.
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  • Find a TensorFlow learning group online or in your local area.
  • Attend the group's meetings regularly.
  • Participate in discussions and ask questions.
Complete Coding Exercises
Regular practice with coding exercises will help you develop proficiency in TensorFlow and machine learning.
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  • Find a set of coding exercises online or in a textbook.
  • Break down each exercise into smaller steps.
  • Write the code and test it.
  • Debug any errors and improve your solution.
Follow Online Tutorials
Following online tutorials can help you supplement the material covered in the course and learn at your own pace.
Browse courses on TensorFlow
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  • Find a reputable online course or tutorial platform.
  • Choose a tutorial that covers a topic you're interested in.
  • Follow the tutorial step-by-step and complete the exercises.
  • Ask questions in the discussion forum or to the instructor if you get stuck.
Practice Coding in TensorFlow
The course requires you to apply TensorFlow in coding, so this activity will help you improve your coding skills in this framework.
Browse courses on TensorFlow
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  • Find online coding exercises or tutorials that use TensorFlow.
  • Go through the exercises and try to solve them on your own.
  • Debug your code and try to optimize it.
Attend a TensorFlow Workshop
Attending a TensorFlow workshop can give you a deeper understanding of the framework and its applications.
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  • Find a TensorFlow workshop in your area.
  • Register for the workshop.
  • Attend the workshop and participate in the exercises.
  • Network with other TensorFlow users and experts.
Build a Machine Learning Project
Building a machine learning project will give you hands-on experience applying the concepts you learn in the course.
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  • Choose a project idea that interests you.
  • Gather the necessary data and resources.
  • Design and implement your machine learning model.
  • Evaluate the performance of your model.
  • Write a report or presentation about your project.
Contribute to an Open-Source TensorFlow Project
Contributing to an open-source TensorFlow project can give you hands-on experience using the framework and help you learn from others.
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  • Find an open-source TensorFlow project that interests you.
  • Read the project documentation and contribute according to the project guidelines.
  • Submit your contributions for review.
  • Respond to feedback and make changes as necessary.
Write a TensorFlow Tutorial
The course covers complex concepts, so creating a tutorial will help solidify your understanding and aid in retention.
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  • Choose a topic that you are comfortable with.
  • Write a clear and concise tutorial on the topic.
  • Share your tutorial with others.
Build a TensorFlow Project
The course introduces you to the fundamentals of TensorFlow, and this activity will allow you to apply what you've learned to a practical project.
Browse courses on TensorFlow
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  • Choose a project idea that interests you.
  • Design and implement your project using TensorFlow.
  • Test and evaluate your project.
Mentor a Junior TensorFlow Developer
You will have a deeper understanding of the concepts you've learned in the course by explaining them to someone else.
Browse courses on TensorFlow
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  • Find a junior TensorFlow developer who is looking for a mentor.
  • Meet with your mentee regularly to discuss TensorFlow concepts.
  • Provide guidance and support to your mentee.

Career center

Learners who complete Building Regression Models Using TensorFlow 1 will develop knowledge and skills that may be useful to these careers:
Fraud Analyst
A Fraud Analyst investigates potentially fraudulent activities and identifies potential fraud schemes. This course is very helpful as regression models may be used to detect fraud or predict the likelihood of fraud based on historical data.
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models. This course is very helpful as Machine Learning Engineers build and deploy regression models that use historical data to predict future outcomes.
Operations Research Analyst
An Operations Research Analyst uses advanced analytical techniques to solve complex business problems. This course is very helpful as regression models are statistical models that may be used to solve complex business problems.
Statistician
A Statistician collects, analyzes, and interprets data to draw conclusions. This course is very helpful for Statisticians who want to develop and use regression models to analyze data and draw conclusions.
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course is very helpful for Software Engineers who want to use regression models to improve the performance of their software.
Researcher
A Researcher conducts scientific research to answer questions and develop new knowledge. This course is very helpful for Researchers who want to use regression models to analyze data and draw conclusions.
Insurance Analyst
An Insurance Analyst works for insurance companies and helps underwrite and price insurance policies. This course is very helpful as regression models are statistical models that may be used to underwrite or price insurance policies.
Data Scientist
A Data Scientist develops new solutions leveraging data, statistics, and machine learning. This course is very helpful as Data Scientists build and deploy regression models as part of their work, which is discussed deeply in this course.
Financial Analyst
A Financial Analyst provides investment advice, performs due diligence, and makes investment recommendations. This course is very helpful as regression models are statistical models that may be used to inform decision-making and predictions in financial analysis.
Risk Analyst
A Risk Analyst identifies and assesses risks to an organization. This course is very helpful for Risk Analysts who want to use regression models to identify and assess risks.
Product Manager
A Product Manager is responsible for the development and management of a product or service. This course is very helpful for Product Managers who want to use data to make better decisions about their products or services.
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze and predict financial data. This course is very helpful for Quantitative Analysts who want to develop and use regression models to make better predictions about financial data.
Data Analyst
A Data Analyst designs and implements systems to retrieve and analyze data for storage, retrieval and reporting so others can make better decisions and take better actions. This course may be useful as Data Analysts often work with structured data with the goal of automating processes. This is related to regression models that automate processes and actions based on historical data.
Web Developer
A Web Developer designs and develops websites and web applications. This course may be useful as Web Developers may implement regression models on the web, typically as a service or for predictions.
Data Engineer
A Data Engineer implements data integration and management solutions including data ingestion and cleansing. This course may be useful as Data Engineers often build and maintain data pipelines, which is related to regression models that use historical data to predict future outcomes.

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 Building Regression Models Using TensorFlow 1.
Provides a practical introduction to machine learning using Python. It covers the basics of machine learning, including data preprocessing, model training, and evaluation. It also includes hands-on examples and exercises.
Provides a collection of recipes for solving common problems with TensorFlow. It valuable resource for anyone looking to learn how to use TensorFlow effectively.
Provides a practical introduction to deep learning using R. It covers the basics of neural networks, training, and evaluation, and includes hands-on examples and exercises.
Provides a concise overview of TensorFlow. It covers the basics of the framework, including data loading, model training, and evaluation. It valuable resource for anyone looking to get started with TensorFlow.
Provides a practical introduction to deep learning using Java. It covers the basics of neural networks, training, and evaluation, and includes hands-on examples and exercises.
Provides a collection of recipes for solving common problems with TensorFlow 2.0. It covers a wide range of topics, including data preprocessing, model training, and evaluation. It valuable resource for anyone looking to learn how to use TensorFlow 2.0 effectively.
Provides a practical introduction to deep learning using Swift. It covers the basics of neural networks, training, and evaluation, and includes hands-on examples and exercises.
Provides a practical introduction to deep learning using JavaScript. It covers the basics of neural networks, training, and evaluation, and includes hands-on examples and exercises.
Provides a quick and easy introduction to TensorFlow 2.0. It covers the basics of the framework, including data loading, model training, and evaluation. It valuable resource for anyone looking to get started with TensorFlow 2.0.

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