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Amit Yadav
In this 2-hours long project-based course, you will learn how to implement a Neural Network model in TensorFlow using its core functionality (i.e. without the help of a high level API like Keras). You will also implement the gradient descent algorithm with the help of TensorFlow's automatic differentiation. While it’s easier to get started with TensorFlow with the Keras API, it’s still worth understanding how a slightly lower level implementation might work in tensorflow, and this project will give you a great starting point.
Read more
In this 2-hours long project-based course, you will learn how to implement a Neural Network model in TensorFlow using its core functionality (i.e. without the help of a high level API like Keras). You will also implement the gradient descent algorithm with the help of TensorFlow's automatic differentiation. While it’s easier to get started with TensorFlow with the Keras API, it’s still worth understanding how a slightly lower level implementation might work in tensorflow, and this project will give you a great starting point.
Read more
In this 2-hours long project-based course, you will learn how to implement a Neural Network model in TensorFlow using its core functionality (i.e. without the help of a high level API like Keras). You will also implement the gradient descent algorithm with the help of TensorFlow's automatic differentiation. While it’s easier to get started with TensorFlow with the Keras API, it’s still worth understanding how a slightly lower level implementation might work in tensorflow, and this project will give you a great starting point.
In order to be successful in this project, you should be familiar with python programming, TensorFlow basics, conceptual understanding of Neural Networks and gradient descent.
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/cd1i4o/neural
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What's inside
Syllabus
Neural Network from Scratch in TensorFlow
Welcome to Neural Network from Scratch in TensorFlow! In this 2-hours long project-based course, you will learn how to implement a Neural Network model in TensorFlow using its core functionality (i.e. without the help of a high level API like Keras). You will also implement the gradient descent algorithm with the help of TensorFlow's automatic differentiation. While it’s easier to get started with TensorFlow with the Keras API, it’s still worth understanding how a slightly lower level implementation might work in tensorflow, and this project will give you a great starting point for the same.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers core concepts and skills in neural networks and TensorFlow, which are highly relevant in industry and academia
Provides hands-on practice implementing a neural network model from scratch in TensorFlow, building a strong foundation for learners
Utilizes TensorFlow's automatic differentiation for implementing the gradient descent algorithm
Suitable for intermediate learners with familiarity in Python programming, TensorFlow basics, and understanding of neural networks
Led by instructors with expertise in the field, ensuring reliable and up-to-date content
Note: Course is currently optimized for learners in the North America region
Save this course
Save Neural Network from Scratch in TensorFlow to your list so you can find it easily later:
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Reviews summary
Neural networks fundamentals
According to students, this project-based TensorFlow course is a good and engaging introduction to building neural networks from scratch. The practical nature of the course is largely positive with many learners saying that they appreciate the hands on experience. The instructor presents the material in an organized way and provides a clear explanation of concepts. Students do note that some extra self-directed learning may be needed and the platform for running the project was slow for some users.
Instructor breaks down concepts in a clear manner.
"The instructor has explained each line of code and this helped me understand the context better and I was able to keep track of things with ease."
"Excellent course if you want to learn how to build a basic neural network with tensorflow."
"The instructor's explanations are very clear."
Heavy emphasis on hands on learning.
"Best hands-on experience.The understanding was awesome. Keep making these types of projects."
"nice i apprecitae"
"Excellent Guided Project"
Technical issues with running code.
"The course content was good. But I a technical problem withe the course, my browser crashed during the lecture and I couldn't resume my lecture."
"The platform, being on the cloud was working very slow for me (due to slow internet)"
Additional research is advised to fill in gaps.
"This course goes through the project step by step and demands an extra effort of clarifying your doubts through a google search."
"It's neither a very advanced level nor a very basic level neural network course."
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 Neural Network from Scratch in TensorFlow with these
activities:
TensorFlow Basics Review
Show steps
Refresh your understanding of TensorFlow basics to strengthen your foundation for this course.
Browse courses on
TensorFlow
Show steps
-
Review the TensorFlow website
-
Read through the TensorFlow documentation
-
Complete a few basic TensorFlow tutorials
Read 'Deep Learning with Python' by Francois Chollet
Show steps
Provides a comprehensive guide to deep learning concepts and hands-on experience with Python and TensorFlow.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
-
Read the first four chapters to gain a foundational understanding of deep learning.
-
Work through the tutorials and exercises in the book to practice using Python and TensorFlow.
-
Complete the end-of-chapter quizzes to test your comprehension.
Read 'TensorFlow for Deep Learning' by Bharath Ramsundar and Reza Bosagh Zadeh
Show steps
Provides a comprehensive overview of TensorFlow, helping you understand its core concepts and practical applications for deep learning.
View
TensorFlow for Deep Learning: From Linear...
on Amazon
Show steps
-
Read the first three chapters to gain a foundational understanding of TensorFlow.
-
Work through the tutorials and exercises in the book to practice using TensorFlow.
-
Complete the end-of-chapter quizzes to test your comprehension.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow along with video tutorials on building neural networks in TensorFlow
Show steps
Provides visual and interactive learning experiences to complement the course content.
Browse courses on
Neural Networks
Show steps
-
Search for video tutorials on building neural networks in TensorFlow.
-
Follow along with the tutorials, taking notes and practicing the code.
TensorFlow Guided Tutorials
Show steps
Explore additional TensorFlow tutorials to enhance your understanding of specific concepts.
Browse courses on
TensorFlow
Show steps
-
Search for TensorFlow tutorials on YouTube
-
Complete a few recommended tutorials
Complete TensorFlow tutorials on gradient descent
Show steps
Strengthens your understanding of gradient descent and how to implement it in TensorFlow.
Browse courses on
Gradient Descent
Show steps
-
Follow the official TensorFlow tutorial on gradient descent.
-
Complete the practice exercises provided in the tutorial.
Neural Network Code Along
Show steps
Practice implementing a neural network in TensorFlow to improve your understanding of the core functionality.
Browse courses on
Neural Networks
Show steps
-
Follow along with the code demonstrated in the course
-
Build a simple neural network from scratch
Join a study group to discuss course concepts and work on projects together
Show steps
Provides opportunities for collaboration, peer learning, and reinforcement of course material.
Browse courses on
Neural Networks
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss course material and work on projects.
-
Help each other understand concepts and troubleshoot problems.
Gradient Descent Implementation
Show steps
Create a project that implements the gradient descent algorithm using TensorFlow's automatic differentiation to solidify your understanding.
Browse courses on
Gradient Descent
Show steps
-
Design the architecture of your gradient descent implementation
-
Code the implementation in TensorFlow
-
Test and refine your implementation
Create a small neural network project using TensorFlow to solve a real-world problem
Show steps
Applies course concepts to a practical scenario, fostering problem-solving skills and project-based learning.
Browse courses on
Neural Networks
Show steps
-
Identify a real-world problem that you can solve using a neural network.
-
Design and implement a neural network model using TensorFlow.
-
Train and evaluate your model.
Create a blog post explaining neural networks and their implementation in TensorFlow
Show steps
Enhances your comprehension of neural networks and helps you articulate your knowledge effectively.
Browse courses on
Neural Networks
Show steps
-
Research neural networks and their implementation in TensorFlow.
-
Write a blog post explaining the concepts in a clear and concise manner.
-
Share your blog post online and invite feedback from others.
TensorFlow Basics Review
Show steps
Refresh your understanding of TensorFlow basics to strengthen your foundation for this course.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review the TensorFlow website
- Read through the TensorFlow documentation
- Complete a few basic TensorFlow tutorials
Read 'Deep Learning with Python' by Francois Chollet
Show steps
Provides a comprehensive guide to deep learning concepts and hands-on experience with Python and TensorFlow.
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
Show steps
Show steps
- Read the first four chapters to gain a foundational understanding of deep learning.
- Work through the tutorials and exercises in the book to practice using Python and TensorFlow.
- Complete the end-of-chapter quizzes to test your comprehension.
Read 'TensorFlow for Deep Learning' by Bharath Ramsundar and Reza Bosagh Zadeh
Show steps
Provides a comprehensive overview of TensorFlow, helping you understand its core concepts and practical applications for deep learning.
View
TensorFlow for Deep Learning: From Linear...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters to gain a foundational understanding of TensorFlow.
- Work through the tutorials and exercises in the book to practice using TensorFlow.
- Complete the end-of-chapter quizzes to test your comprehension.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow along with video tutorials on building neural networks in TensorFlow
Show steps
Provides visual and interactive learning experiences to complement the course content.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Search for video tutorials on building neural networks in TensorFlow.
- Follow along with the tutorials, taking notes and practicing the code.
TensorFlow Guided Tutorials
Show steps
Explore additional TensorFlow tutorials to enhance your understanding of specific concepts.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Search for TensorFlow tutorials on YouTube
- Complete a few recommended tutorials
Complete TensorFlow tutorials on gradient descent
Show steps
Strengthens your understanding of gradient descent and how to implement it in TensorFlow.
Browse courses on
Gradient Descent
Show steps
Show steps
Show steps
- Follow the official TensorFlow tutorial on gradient descent.
- Complete the practice exercises provided in the tutorial.
Neural Network Code Along
Show steps
Practice implementing a neural network in TensorFlow to improve your understanding of the core functionality.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Follow along with the code demonstrated in the course
- Build a simple neural network from scratch
Join a study group to discuss course concepts and work on projects together
Show steps
Provides opportunities for collaboration, peer learning, and reinforcement of course material.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss course material and work on projects.
- Help each other understand concepts and troubleshoot problems.
Gradient Descent Implementation
Show steps
Create a project that implements the gradient descent algorithm using TensorFlow's automatic differentiation to solidify your understanding.
Browse courses on
Gradient Descent
Show steps
Show steps
Show steps
- Design the architecture of your gradient descent implementation
- Code the implementation in TensorFlow
- Test and refine your implementation
Create a small neural network project using TensorFlow to solve a real-world problem
Show steps
Applies course concepts to a practical scenario, fostering problem-solving skills and project-based learning.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Identify a real-world problem that you can solve using a neural network.
- Design and implement a neural network model using TensorFlow.
- Train and evaluate your model.
Create a blog post explaining neural networks and their implementation in TensorFlow
Show steps
Enhances your comprehension of neural networks and helps you articulate your knowledge effectively.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Research neural networks and their implementation in TensorFlow.
- Write a blog post explaining the concepts in a clear and concise manner.
- Share your blog post online and invite feedback from others.
Career center
Learners who complete Neural Network from Scratch in TensorFlow will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in developing and applying deep learning models. Neural networks are the most common type of deep learning model, and this course will give you the skills you need to succeed in this field.
AI Engineer
AI Engineer
AI Engineers design, build, and maintain AI systems. Neural networks are a fundamental component of many AI systems. This course will teach you how to implement and train neural networks from scratch, which will give you a significant advantage in this field.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop systems that can see and understand the world around them. Neural networks are a key technology in computer vision, and this course will give you the skills you need to succeed in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning solutions. This course will help you gain a deeper understanding of how neural networks work, which is critical for success in this field.
Data Scientist
Data Scientist
Data Scientists build models to predict outcomes. Neural networks are among the most common and powerful types of models used by Data Scientists. By taking this course, you will become familiar with the underlying implementation of neural networks, which will help you to design and train more powerful models.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design and develop systems that can understand and process human language. Neural networks are commonly used in NLP applications, such as machine translation and spam filtering. This course will give you the skills you need to work with neural networks in NLP applications.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Neural networks are increasingly being used in software applications, such as image recognition and natural language processing. This course will give you the skills you need to work with neural networks in a software engineering environment.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. Neural networks are increasingly being used for quantitative analysis, such as predicting stock prices and managing risk. This course will give you the skills you need to work with neural networks for quantitative analysis projects.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and services. Neural networks are increasingly being used for product development, such as predicting customer demand and identifying new opportunities. This course will give you the skills you need to work with neural networks for product development projects.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts help businesses make better decisions by providing them with insights into their data. Neural networks are increasingly being used for business intelligence, such as predicting customer behavior and identifying trends. This course will give you the skills you need to work with neural networks for business intelligence projects.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. Neural networks are increasingly being used for operations research, such as optimizing routing and scheduling. This course will give you the skills you need to work with neural networks for operations research projects.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. Neural networks are increasingly being used for statistics, such as predicting outcomes and identifying patterns. This course will give you the skills you need to work with neural networks for statistical projects.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make recommendations for investors. Neural networks are increasingly being used for financial analysis, such as predicting stock prices and identifying trends. This course will give you the skills you need to work with neural networks for financial analysis projects.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. Neural networks are increasingly being used for data analysis, such as fraud detection and customer segmentation. This course will give you the skills you need to work with neural networks for data analysis projects.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze marketing data to help businesses make better decisions. Neural networks are increasingly being used for marketing analysis, such as predicting customer behavior and identifying trends. This course will give you the skills you need to work with neural networks for marketing analysis projects.
For more career information including salaries, visit:
OpenCourser.com/course/cd1i4o/neural
Reading list
We've selected six 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
Neural Network from Scratch in TensorFlow.
Provides a comprehensive overview of deep learning and its applications in Python. It covers the core concepts of neural networks, including the gradient descent algorithm and backpropagation.
Provides a hands-on guide to using TensorFlow 2 and Keras for deep learning. It covers the basics of TensorFlow and Keras, as well as how to create and train neural networks for a variety of tasks.
Provides a practical guide to using TensorFlow for machine learning. It covers the basics of TensorFlow, as well as how to create and train models for a variety of tasks.
Save
Provides a comprehensive overview of deep learning. It covers the basics of deep learning, including the gradient descent algorithm and backpropagation.
Provides a comprehensive overview of pattern recognition and machine learning. It covers the basics of pattern recognition and machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of TensorFlow for R developers. It covers the basics of TensorFlow, including how to create and train neural networks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cd1i4o/neural
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Covers core concepts and skills in neural networks and TensorFlow, which are highly relevant in industry and academia
Provides hands-on practice implementing a neural network model from scratch in TensorFlow, building a strong foundation for learners
Utilizes TensorFlow's automatic differentiation for implementing the gradient descent algorithm
Suitable for intermediate learners with familiarity in Python programming, TensorFlow basics, and understanding of neural networks
Led by instructors with expertise in the field, ensuring reliable and up-to-date content
Note: Course is currently optimized for learners in the North America region
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