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Parth Dhameliya
In this 2-hour long guided-project course, you will learn how to implement a Siamese Network, you will train the network with the Triplet loss function. You will create Anchor, Positive and Negative image dataset, which will be the inputs of triplet loss function, through which the network will learn feature embeddings. Siamese Network have plethora of applications such as face recognition, signature checking, person re-identification, etc. In this project, you will train a simple Siamese Network for person re-identification.
Register for this course and see more details by visiting:
OpenCourser.com/course/utwkz9/deep
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What's inside
Syllabus
Project Overview
In this 2-hour long guided-project course, you will learn how to implement a Siamese Network, you will train the network with the Triplet loss function. You will create Anchor, Positive and Negative image dataset, which will be the inputs of triplet loss function, through which the network will learn feature embeddings. Siamese Network have plethora of applications such as face recognition, signature checking, person re-identification, etc. In this project, you will train a simple Siamese Network for person re-identification.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores Siamese Networks, which are widely applicable in areas like person re-identification and face recognition
Involves implementation of a Siamese Network using Triplet loss function, providing hands-on experience
Suitable for learners interested in gaining practical understanding of Siamese Networks for image-based tasks
Save this course
Save Deep Learning with PyTorch : Siamese Network to your list so you can find it easily later:
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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 Deep Learning with PyTorch : Siamese Network with these
activities:
Deep Learning for Computer Vision
Show steps
Gain a comprehensive understanding of deep learning techniques for computer vision, including Siamese Networks.
View
Concurrent NumPy in Python: Faster NumPy With...
on Amazon
Show steps
-
Read chapters on Siamese Networks and their applications.
-
Complete exercises and practice problems related to Siamese Networks.
-
Apply the concepts to your own computer vision projects.
Siamese Network Project Proposal
Show steps
Plan and outline a project that will challenge your understanding of Siamese Networks and their applications.
Show steps
-
Define the scope and objectives of your project.
-
Identify the dataset you will use.
-
Outline the methodology and algorithms you will employ.
-
Establish a timeline for completion.
Code Siamese Network in Python
Show steps
Reinforce your understanding of Siamese Networks by implementing your own in Python.
Show steps
-
Install PyTorch and other required libraries.
-
Create a custom dataset with Anchor, Positive, and Negative images.
-
Define the Siamese Network architecture using PyTorch.
-
Implement the Triplet loss function.
-
Train the Siamese Network on your custom dataset.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Collaborative Debugging Session
Show steps
Seek support and learn from peers by engaging in a collaborative debugging session focused on Siamese Networks.
Show steps
-
Present your challenges or bugs to the group.
-
Collaborate on finding solutions and understanding the underlying concepts.
Siamese Network Code Challenges
Show steps
Test your proficiency and identify areas for improvement by solving coding challenges related to Siamese Networks.
Show steps
-
Solve coding problems on platforms like LeetCode or HackerRank.
-
Participate in coding competitions focused on Siamese Networks.
Build a Person Re-identification System
Show steps
Enhance your practical skills by building a real-world application that leverages Siamese Networks for person re-identification.
Show steps
-
Explore existing tutorials and resources on person re-identification.
-
Gather and prepare a dataset for person re-identification.
-
Train and evaluate a Siamese Network using the dataset.
-
Deploy the person re-identification system and test its performance.
Siamese Network Workshop
Show steps
Attend an in-person or online workshop to gain hands-on experience and insights from experts in the field of Siamese Networks.
Show steps
-
Research and identify reputable workshops.
-
Register and attend the workshop.
-
Engage with speakers and ask questions.
-
Implement the techniques learned in your own projects.
Siamese Network Innovation Challenge
Show steps
Push your skills to the limit and demonstrate your mastery by participating in a competition that rewards innovative applications of Siamese Networks.
Show steps
-
Identify relevant competitions.
-
Form a team and develop a novel project.
-
Submit your project and present it to a panel of judges.
Deep Learning for Computer Vision
Show steps
Gain a comprehensive understanding of deep learning techniques for computer vision, including Siamese Networks.
View
Concurrent NumPy in Python: Faster NumPy With...
on Amazon
Show steps
Show steps
Show steps
- Read chapters on Siamese Networks and their applications.
- Complete exercises and practice problems related to Siamese Networks.
- Apply the concepts to your own computer vision projects.
Siamese Network Project Proposal
Show steps
Plan and outline a project that will challenge your understanding of Siamese Networks and their applications.
Show steps
Show steps
Show steps
- Define the scope and objectives of your project.
- Identify the dataset you will use.
- Outline the methodology and algorithms you will employ.
- Establish a timeline for completion.
Code Siamese Network in Python
Show steps
Reinforce your understanding of Siamese Networks by implementing your own in Python.
Show steps
Show steps
Show steps
- Install PyTorch and other required libraries.
- Create a custom dataset with Anchor, Positive, and Negative images.
- Define the Siamese Network architecture using PyTorch.
- Implement the Triplet loss function.
- Train the Siamese Network on your custom dataset.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Collaborative Debugging Session
Show steps
Seek support and learn from peers by engaging in a collaborative debugging session focused on Siamese Networks.
Show steps
Show steps
Show steps
- Present your challenges or bugs to the group.
- Collaborate on finding solutions and understanding the underlying concepts.
Siamese Network Code Challenges
Show steps
Test your proficiency and identify areas for improvement by solving coding challenges related to Siamese Networks.
Show steps
Show steps
Show steps
- Solve coding problems on platforms like LeetCode or HackerRank.
- Participate in coding competitions focused on Siamese Networks.
Build a Person Re-identification System
Show steps
Enhance your practical skills by building a real-world application that leverages Siamese Networks for person re-identification.
Show steps
Show steps
Show steps
- Explore existing tutorials and resources on person re-identification.
- Gather and prepare a dataset for person re-identification.
- Train and evaluate a Siamese Network using the dataset.
- Deploy the person re-identification system and test its performance.
Siamese Network Workshop
Show steps
Attend an in-person or online workshop to gain hands-on experience and insights from experts in the field of Siamese Networks.
Show steps
Show steps
Show steps
- Research and identify reputable workshops.
- Register and attend the workshop.
- Engage with speakers and ask questions.
- Implement the techniques learned in your own projects.
Siamese Network Innovation Challenge
Show steps
Push your skills to the limit and demonstrate your mastery by participating in a competition that rewards innovative applications of Siamese Networks.
Show steps
Show steps
Show steps
- Identify relevant competitions.
- Form a team and develop a novel project.
- Submit your project and present it to a panel of judges.
Career center
Learners who complete Deep Learning with PyTorch : Siamese Network will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
A Deep Learning Engineer specializes in developing and implementing deep learning models for various applications. This course may be useful for an aspiring Deep Learning Engineer because it provides hands-on experience with training a Siamese Network using PyTorch, a popular deep learning framework.
Machine Learning Specialist
Machine Learning Specialist
A Machine Learning Specialist focuses on developing and implementing machine learning solutions for various applications. This course may be useful for an aspiring Machine Learning Specialist because it provides practical experience with training a Siamese Network, a type of neural network used in machine learning applications such as person re-identification.
Computer Scientist
Computer Scientist
A Computer Scientist researches, designs, and develops computer systems and applications. This course may be useful for an aspiring Computer Scientist because it provides an understanding of Siamese Networks, a type of neural network used in various computer science applications, including computer vision and natural language processing.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops, and deploys artificial intelligence systems. This course may be useful for an aspiring Artificial Intelligence Engineer because it provides exposure to Siamese Networks, a type of neural network used in AI applications such as person re-identification.
AI Researcher
AI Researcher
An AI Researcher develops new methods and algorithms for artificial intelligence, focusing on areas such as machine learning, computer vision, and natural language processing. This course may be useful for an aspiring AI Researcher because it provides exposure to Siamese Networks, a type of neural network used in various AI applications, including person re-identification.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to uncover trends and patterns, providing insights to businesses. This course may be useful for an aspiring Data Analyst because it provides an understanding of Siamese Networks, a type of neural network used for tasks such as image analysis and classification.
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer develops and implements computer vision systems, which enable computers to interpret and understand images and videos. This course may be useful for an aspiring Computer Vision Engineer because it provides hands-on experience with training a Siamese Network, a type of neural network used in computer vision applications such as person re-identification.
Bioinformatician
Bioinformatician
A Bioinformatics develops and applies computational tools and techniques to analyze biological data. This course may be useful for an aspiring Bioinformatics because it provides an understanding of Siamese Networks, a type of neural network used in bioinformatics applications such as image analysis and data classification.
Data Scientist
Data Scientist
A Data Scientist uses data analysis techniques to extract insights from data, helping businesses make informed decisions. This course may be useful for an aspiring Data Scientist because it provides an understanding of Siamese Networks, a type of neural network used for tasks such as person re-identification, which involve working with image data.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and databases. This course may be useful for an aspiring Data Engineer because it provides an understanding of Siamese Networks, a type of neural network used for tasks such as data analysis and data mining.
Robotics Engineer
Robotics Engineer
A Robotics Engineer designs, builds, and maintains robots. This course may be useful for an aspiring Robotics Engineer because it provides an understanding of Siamese Networks, a type of neural network used in robotics applications such as object recognition and human-robot interaction.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. This course may be useful for an aspiring Quantitative Analyst because it provides an understanding of Siamese Networks, a type of neural network used in quantitative finance applications such as risk management and portfolio optimization.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer constructs, deploys, and maintains machine learning systems, which analyze data to uncover patterns and automate tasks. This course may be useful for an aspiring Machine Learning Engineer because it provides an understanding of Siamese Networks, a type of neural network used for tasks such as person re-identification.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical methods to solve complex business problems. This course may be useful for an aspiring Operations Research Analyst because it provides an understanding of Siamese Networks, a type of neural network used in operations research applications such as optimization and decision-making.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be useful for an aspiring Software Engineer because it provides practical experience with implementing a Siamese Network in PyTorch, a popular deep learning framework.
For more career information including salaries, visit:
OpenCourser.com/course/utwkz9/deep
Reading list
We've selected 14 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
Deep Learning with PyTorch : Siamese Network.
Provides a comprehensive overview of deep learning concepts and techniques, including convolutional neural networks and recurrent neural networks.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It also includes practical examples and exercises to help readers apply their learning.
Provides a comprehensive overview of reinforcement learning, covering topics such as Markov decision processes, value functions, and reinforcement learning algorithms. It also includes practical examples and exercises to help readers apply their learning.
Save
Provides a comprehensive overview of computer vision, covering topics such as image formation, image processing, and computer vision applications. It also includes practical examples and exercises to help readers apply their learning.
Provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language understanding, and speech synthesis. It also includes practical examples and exercises to help readers apply their learning.
Provides a comprehensive overview of deep learning for natural language processing, covering topics such as text classification, sentiment analysis, and machine translation. It also includes practical examples and exercises to help readers apply their learning.
Covers a wide range of deep learning models and techniques tailored for computer vision tasks, including Siamese networks.
Provides a comprehensive overview of natural language processing with Python, covering topics such as text processing, natural language understanding, and natural language generation. It also includes practical examples and exercises to help readers apply their learning.
Provides a practical introduction to PyTorch, covering topics such as PyTorch basics, PyTorch operations, and PyTorch models. It also includes practical examples and exercises to help readers apply their learning.
Save
Offers a foundational understanding of Generative Adversarial Networks (GANs), which can be used in conjunction with Siamese networks for various image-related tasks.
Provides a practical introduction to machine learning with Python, covering topics such as data preprocessing, model selection, and evaluation. It also includes hands-on exercises and projects to help readers apply their learning.
Provides a practical introduction to computer vision with Python, covering topics such as image processing, feature extraction, and object detection. It also includes hands-on exercises and projects to help readers apply their learning.
Save
Serves as a comprehensive reference for OpenCV, covering a wide range of computer vision topics, enhancing the learner's understanding of image processing techniques.
Covers the basics of OpenCV for image processing and computer vision tasks, providing a useful reference for implementing the practical aspects of Siamese networks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/utwkz9/deep
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Effort
2 hours
Level
Advanced
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Good to know
Explores Siamese Networks, which are widely applicable in areas like person re-identification and face recognition
Involves implementation of a Siamese Network using Triplet loss function, providing hands-on experience
Suitable for learners interested in gaining practical understanding of Siamese Networks for image-based tasks
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