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Amit Yadav
In this 2 hour long project-based course, you will learn to create a Neural Network Visualizer web application using Streamlit, and a simple model server using Keras and Flask. You will also use Keras to train a Neural Network model, and use Keras' functional API to create a model with multiple outputs. We will create a web application that will visualize the outputs of all the nodes of all the layers of the neural network for a given input image.
Read more
In this 2 hour long project-based course, you will learn to create a Neural Network Visualizer web application using Streamlit, and a simple model server using Keras and Flask. You will also use Keras to train a Neural Network model, and use Keras' functional API to create a model with multiple outputs. We will create a web application that will visualize the outputs of all the nodes of all the layers of the neural network for a given input image.
Read more
In this 2 hour long project-based course, you will learn to create a Neural Network Visualizer web application using Streamlit, and a simple model server using Keras and Flask. You will also use Keras to train a Neural Network model, and use Keras' functional API to create a model with multiple outputs. We will create a web application that will visualize the outputs of all the nodes of all the layers of the neural network for a given input image.
In order to complete this project successfully, you will need prior programming experience with Python, understanding of the theory behind neural networks, and familiarity with Keras.
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/uwpyx8/neural
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Syllabus
Neural Network Visualizer Web App with Python
In this 2 hour long project-based course, you will learn to create a Neural Network Visualizer web application using Streamlit, and a simple model server using Keras and Flask. You will also use Keras to train a Neural Network model, and use Keras' functional API to create a model with multiple outputs. We will create a web application that will visualize the outputs of all the nodes of all the layers of the neural network for a given input image.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches neural networks, which are a core technique in AI
Uses Python, Keras, and Flask, which are established in the industry
Develops and strengthens skills in data science and machine learning
May require students to purchase additional resources
Requires some prior knowledge and experience with Python and Keras
May not be suitable for students without experience in data science or machine learning
Save this course
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Reviews summary
Positive intro to creating neural network viz web app with python
Learners largely agree that this course is very good for becoming familiar with how to create a Neural Networks Visualizer Web App using Python. Many recommend this course for beginners to get hands-on experience with Streamlit and Flask by following along with an easy-to-follow guided project. However, some learners mention that they would have liked to dive deeper into specific aspects of the technology and encountered some issues when implementing the code.
Suitable for beginners
"Very useful course for me"
"good experience"
"easy to understand"
"This project helped me to..."
Helpful guided project
"A helpful guided project that explained..."
"Excellent project .A nice way to..."
"This is a great project!"
Well prepared instructor
"The instructor was to the point..."
"Instructor explained each and every..."
"Instructor has been truly fantastic."
Could be more in-depth
"This project used relatively little Streamlit..."
"It didn’t dive in deep to any particular..."
Encountered some bugs
"I just regret not getting my files"
"project was well guided but doubt solving..."
"This is not exactly a great 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 Visualizer Web App with Python with these
activities:
Review Linear Algebra and Calculus
Show steps
Linear algebra and calculus are foundational concepts for understanding neural networks. Reviewing these topics will strengthen your mathematical background.
Browse courses on
Linear Algebra
Show steps
-
Review linear algebra concepts such as matrices, vectors, and transformations
-
Refresh your calculus knowledge, including derivatives and integrals
Attend Study Group on Neural Network Architectures
Show steps
Participating in study groups provides opportunities to discuss and clarify concepts, exchange ideas, and enhance your understanding of neural network architectures.
Browse courses on
Neural Networks
Show steps
-
Find a study group or organize one with peers
-
Discuss different neural network architectures and their applications
Read 'Deep Learning with Python'
Show steps
This book provides a comprehensive overview of deep learning concepts and techniques using Python and the TensorFlow library, which will enhance your understanding of the course material.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read each chapter thoroughly, taking notes on key concepts
-
Work through the provided exercises and examples
Six other activities
Expand to see all activities and additional details
Show all nine activities
Contribute to the Keras Community
Show steps
Contributing to the Keras community through reporting bugs, suggesting improvements, or adding new features will enhance your understanding of its inner workings and provide valuable hands-on experience.
Browse courses on
Keras
Show steps
-
Identify an area where you can contribute
-
Submit a bug report or feature request
-
Contribute code or documentation
Complete Keras Tutorial on Tensorflow
Show steps
This tutorial provides a practical understanding of Keras, a popular deep learning library, and its integration with TensorFlow, which will complement the course content.
Browse courses on
Keras
Show steps
-
Follow the Tensorflow tutorial on Keras
-
Experiment with different Keras models and architectures
Practice Building Neural Networks with Keras
Show steps
Regular practice in building neural networks with Keras is crucial for solidifying your understanding and developing proficiency in this area.
Browse courses on
Neural Networks
Show steps
-
Implement various neural network architectures using Keras
-
Experiment with different activation functions, optimizers, and loss functions
Create a Visual Representation of Neural Network Layers
Show steps
Creating a visual representation of the layers of a neural network will enhance your comprehension of their functionality and interconnections.
Browse courses on
Neural Networks
Show steps
-
Choose a neural network model
-
Create a diagram or visualization of each layer and its connections
-
Label the layers and indicate the flow of data
Develop a Neural Network for Image Classification
Show steps
Building a neural network from scratch for image classification will provide hands-on experience and reinforce your understanding of the concepts covered in the course.
Browse courses on
Neural Networks
Show steps
-
Gather and prepare an image dataset
-
Design and train a neural network model
-
Evaluate the performance of your model
Mentor Beginners in Neural Networks
Show steps
Mentoring others allows you to solidify your understanding of neural networks while supporting beginners in their learning journey.
Browse courses on
Neural Networks
Show steps
-
Find opportunities to mentor others
-
Provide guidance and support on neural network concepts
Review Linear Algebra and Calculus
Show steps
Linear algebra and calculus are foundational concepts for understanding neural networks. Reviewing these topics will strengthen your mathematical background.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review linear algebra concepts such as matrices, vectors, and transformations
- Refresh your calculus knowledge, including derivatives and integrals
Attend Study Group on Neural Network Architectures
Show steps
Participating in study groups provides opportunities to discuss and clarify concepts, exchange ideas, and enhance your understanding of neural network architectures.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find a study group or organize one with peers
- Discuss different neural network architectures and their applications
Read 'Deep Learning with Python'
Show steps
This book provides a comprehensive overview of deep learning concepts and techniques using Python and the TensorFlow library, which will enhance your understanding of the course material.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read each chapter thoroughly, taking notes on key concepts
- Work through the provided exercises and examples
Six other activities
Expand to see all activities and additional details
Show all nine activities
Contribute to the Keras Community
Show steps
Contributing to the Keras community through reporting bugs, suggesting improvements, or adding new features will enhance your understanding of its inner workings and provide valuable hands-on experience.
Browse courses on
Keras
Show steps
Show steps
Show steps
- Identify an area where you can contribute
- Submit a bug report or feature request
- Contribute code or documentation
Complete Keras Tutorial on Tensorflow
Show steps
This tutorial provides a practical understanding of Keras, a popular deep learning library, and its integration with TensorFlow, which will complement the course content.
Browse courses on
Keras
Show steps
Show steps
Show steps
- Follow the Tensorflow tutorial on Keras
- Experiment with different Keras models and architectures
Practice Building Neural Networks with Keras
Show steps
Regular practice in building neural networks with Keras is crucial for solidifying your understanding and developing proficiency in this area.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Implement various neural network architectures using Keras
- Experiment with different activation functions, optimizers, and loss functions
Create a Visual Representation of Neural Network Layers
Show steps
Creating a visual representation of the layers of a neural network will enhance your comprehension of their functionality and interconnections.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Choose a neural network model
- Create a diagram or visualization of each layer and its connections
- Label the layers and indicate the flow of data
Develop a Neural Network for Image Classification
Show steps
Building a neural network from scratch for image classification will provide hands-on experience and reinforce your understanding of the concepts covered in the course.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Gather and prepare an image dataset
- Design and train a neural network model
- Evaluate the performance of your model
Mentor Beginners in Neural Networks
Show steps
Mentoring others allows you to solidify your understanding of neural networks while supporting beginners in their learning journey.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find opportunities to mentor others
- Provide guidance and support on neural network concepts
Career center
Learners who complete Neural Network Visualizer Web App with Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts may begin their career with a bachelor's degree, but a master's degree is typically required to enter the field. Those who wish to become Data Analysts can build a foundation through this Neural Network Visualizer Web App with Python course. They can explore the outcomes of all the nodes across all the layers of a neural network for a given input image. This could facilitate their understanding of how data is analyzed, and how to use this information to effectively communicate data-driven insights.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts may begin their career with a bachelor's degree, but many choose to pursue an advanced degree to expand their opportunities. This Neural Network Visualizer Web App with Python course can be helpful for those seeking to become Business Intelligence Analysts. It can help learners understand how neural networks can be used to analyze data, which can enhance their ability to make meaningful business decisions.
Database Administrator
Database Administrator
Database Administrators can enter the field with a bachelor's degree. Those seeking to enter or advance their career may find this Neural Network Visualizer Web App with Python course helpful. It can help them understand how neural networks can be used to analyze data, which can enhance their ability to optimize database design and performance.
Software Developer
Software Developer
A bachelor's degree is typically sufficient to enter the Software Developer career. This course can be an excellent addition to any Software Developer's toolkit. By learning to create a Neural Network Visualizer web application using Streamlit, and a model server using Keras and Flask, they can extend their abilities and broaden their skillset, making them more competitive in the market.
Software Engineer
Software Engineer
Software Engineers may enter the field with a bachelor's degree, but most advance in their career by pursuing a master's or doctoral degree. This course can be an excellent addition to any Software Engineer's toolkit. By learning to create a Neural Network Visualizer web application using Streamlit, and a model server using Keras and Flask, they can extend their abilities and broaden their skillset, making them more competitive in the market.
Computer Programmer
Computer Programmer
Computer Programmers can enter the field with a bachelor's degree. This course can be helpful for those seeking to enter or advance their career as Computer Programmers. By learning to create a Neural Network Visualizer web application using Streamlit, and a model server using Keras and Flask, learners can expand their abilities and broaden their skillset, making them more competitive in the market.
Research Scientist
Research Scientist
Research Scientists often need an advanced degree to enter the field. Those seeking to advance their career as Research Scientists can benefit from this Neural Network Visualizer Web App with Python course. It provides an opportunity to build a strong skillset by learning to create a web application that will visualize the outputs of all the nodes of all the layers of the neural network for a given input image.
Web Developer
Web Developer
Web Developers typically begin with a bachelor's degree, and this course is perfectly suited to help them expand their knowledge base. By learning to use Keras to train a Neural Network model, and using Keras functional API to create a model with multiple outputs, they can explore new ways to approach front-end and back-end web development, which can open up doors to new opportunities and roles.
Data Engineer
Data Engineer
Data Engineers often need an advanced degree to enter the field. Those who wish to become Data Engineers and build a strong foundation in the field can benefit from this Neural Network Visualizer Web App with Python course. They can learn to create a web application that will visualize the outputs of all the nodes of all the layers of the neural network for a given input image, which can extend their knowledge and build confidence in their abilities.
Artificial Intelligence Specialist
Artificial Intelligence Specialist
Artificial Intelligence Specialists may require an advanced degree to enter the field. Those who wish to become Artificial Intelligence Specialists can take this Neural Network Visualizer Web App with Python course to build a foundation in the field. By learning to visualize the outputs of all the nodes across all the layers of a neural network for a given input image, they can develop a deeper understanding of how artificial intelligence works and makes decisions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers may require an advanced degree to enter the field. Those seeking to start or advance their career in this field can benefit from this Neural Network Visualizer Web App with Python course. It provides an opportunity to explore the outputs of all the nodes across all the layers of a neural network for a given input image, which can deepen their understanding of the relationship between neural networks and data analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts require an advanced degree to enter the field. This Neural Network Visualizer Web App with Python course can be taken to further explore the field and enhance one's understanding of neural networks. By learning to use Keras to train Neural Network models and use Keras' functional API to create models with multiple outputs, learners can improve their ability to make data-driven decisions and advance their career as Quantitative Analysts.
Data Scientist
Data Scientist
Data Scientists often need an advanced degree to enter the field. To prepare for a career as a Data Scientist, this Neural Network Visualizer Web App with Python course can be insightful. It allows learners to explore the outputs of all the nodes across all the layers of a neural network for a given input image, helping them develop a strong understanding of neural networks and how they're utilized to analyze and leverage data.
For more career information including salaries, visit:
OpenCourser.com/course/uwpyx8/neural
Reading list
We've selected seven 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 Visualizer Web App with Python.
Is an updated and expanded version of the first edition. It covers a wide range of topics, from data preparation to model deployment.
Is an excellent resource for those who want to learn more about deep learning in Python. It covers a wide range of topics, from the basics of neural networks to more advanced concepts like convolutional neural networks and recurrent neural networks.
Practical guide to machine learning using Python. It covers a wide range of topics, from data preparation to model deployment.
Comprehensive introduction to neural networks and deep learning. It covers a wide range of topics, from the basics of neural networks to more advanced concepts like convolutional neural networks and recurrent neural networks.
Visual guide to deep learning. It uses clear and concise language to explain the concepts of deep learning.
Practical guide to deep learning using Keras. It covers a wide range of topics, from the basics of Keras to more advanced concepts like convolutional neural networks and recurrent neural networks.
Practical guide to machine learning using R. It covers a wide range of topics, from the basics of R to more advanced concepts like deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uwpyx8/neural
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Teaches neural networks, which are a core technique in AI
Uses Python, Keras, and Flask, which are established in the industry
Develops and strengthens skills in data science and machine learning
May require students to purchase additional resources
Requires some prior knowledge and experience with Python and Keras
May not be suitable for students without experience in data science or machine learning
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