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Amit Yadav

In this 2-hour long project-based course, you will learn how to implement a Triplet Loss function, create a Siamese Network, and train the network with the Triplet Loss function. With this training process, the network will learn to produce Embedding of different classes from a given dataset in a way that Embedding of examples from different classes will start to move away from each other in the vector space.

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In this 2-hour long project-based course, you will learn how to implement a Triplet Loss function, create a Siamese Network, and train the network with the Triplet Loss function. With this training process, the network will learn to produce Embedding of different classes from a given dataset in a way that Embedding of examples from different classes will start to move away from each other in the vector space.

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed.

Prerequisites:

In order to be successful in this project, you should be familiar with Python, Keras, Neural Networks.

Notes:

- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.

- 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.

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What's inside

Syllabus

Siamese Network with Triplet Loss in Keras
In this 2-hour long project-based course, you will learn how to implement a Triplet Loss function, create a Siamese Network, and train the network with the Triplet Loss function. With this training process, the network will learn to produce Embedding of different classes from a given dataset in a way that Embedding of examples from different classes will start to move away from each other in the vector space.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Enables students to create Siamese Networks and Triplet Loss functions, which are valuable skills in image recognition and clustering
Provides hands-on practice in implementing a Triplet Loss function, a valuable skill for deep learning practitioners
Suitable for learners with a strong foundation in Python, Keras, and Neural Networks, making it ideal for intermediate-level students
May require additional resources or knowledge for learners without a background in deep learning
Only accessible five times on the cloud desktop, which may be a limitation for learners who need more practice

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Reviews summary

Difficult but focused course

learners say that this course is challenging but focused. Students looking for advanced training in Siamese Networks could benefit from this facets, such as engaging assignments and nuanced explanations. However, learners should note that this course may be incomplete and that the material is difficult to implement. Reviews for this course are mixed.
This course is well-regarded for its rigorous nature.
"a bit challenging yet we want to achieve successfully"
"Test [questions] are not stupid simple questions, but still easy to answer."
"This course has nice content, but the usage is difficult."
This course has engaging assignments and a practical focus.
"I like the way we got involved into practice by setting goals"
"Explanations come to the point and still are deep."
Students have reported that the difficult nature of this course makes it tough to implement
"the videos and the environment were so slow, making it almost impossible to be used"
"Not at all enough to start with a face recognition kind of use cases"
Some students think some important concepts are missing from the course.
"S​afe your money and have a look on this website, it is exactly the same code"
"the prediction is very important not only training!"
"course is not complete. It needs:1- save model2- evaluate model 3- make prediction"

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 Siamese Network with Triplet Loss in Keras with these activities:
Review syllabus and course materials
Helps you refresh your knowledge and expectations for the course materials
Browse courses on Siamese Network
Show steps
  • Read through the syllabus and make a note of important dates and topics
  • Skim through the course materials to get a general overview
Write a blog post or technical report
Encourages you to synthesize your knowledge and share it with others
Browse courses on Siamese Network
Show steps
  • Choose a topic related to Siamese Networks, Triplet Loss, or Keras
  • Research and gather information on the topic
  • Write a well-structured and informative blog post or technical report
Show all two activities

Career center

Learners who complete Siamese Network with Triplet Loss in Keras will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist analyzes data to extract meaningful insights and trends, which can be used to make informed decisions. This course in Siamese Network with Triplet Loss in Keras can be useful for aspiring Data Scientists as it provides a foundation in the techniques used to analyze and process complex data sets, making it a valuable asset in the field.
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve real-world problems. This course in Siamese Network with Triplet Loss in Keras can be useful for aspiring Machine Learning Engineers as it provides hands-on experience in building and training machine learning models, making it a valuable asset in the field.
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course in Siamese Network with Triplet Loss in Keras can be useful for aspiring Software Engineers as it provides a foundation in the principles of software development and design, making it a valuable asset in the field.
Data Analyst
A Data Analyst collects, analyzes, and interprets data to provide insights and recommendations to businesses. This course in Siamese Network with Triplet Loss in Keras can be useful for aspiring Data Analysts as it provides a foundation in the techniques used to analyze and process complex data sets, making it a valuable asset in the field.
Actuary
An Actuary analyzes financial data to assess risk and uncertainty. This course in Siamese Network with Triplet Loss in Keras may be useful for aspiring Actuaries as it provides a foundation in the principles of data analysis and modeling, making it a valuable asset in the field.
Statistician
A Statistician collects, analyzes, and interprets data to provide insights and recommendations. This course in Siamese Network with Triplet Loss in Keras may be useful for aspiring Statisticians as it provides a foundation in the principles of data analysis and modeling, making it a valuable asset in the field.
Business Analyst
A Business Analyst analyzes business processes and systems to identify areas for improvement. This course in Siamese Network with Triplet Loss in Keras may be useful for aspiring Business Analysts as it provides a foundation in the principles of data analysis and interpretation, making it a valuable asset in the field.
Quantitative Analyst
A Quantitative Analyst develops and uses mathematical and statistical models to analyze financial data. This course in Siamese Network with Triplet Loss in Keras may be useful for aspiring Quantitative Analysts as it provides a foundation in the principles of data analysis and modeling, making it a valuable asset in the field.
Operations Research Analyst
An Operations Research Analyst develops and uses mathematical and statistical models to optimize business processes. This course in Siamese Network with Triplet Loss in Keras may be useful for aspiring Operations Research Analysts as it provides a foundation in the principles of data analysis and modeling, making it a valuable asset in the field.
Database Administrator
A Database Administrator designs, builds, and maintains databases. This course in Siamese Network with Triplet Loss in Keras may be useful for aspiring Database Administrators as it provides a foundation in the principles of data management and processing, making it a valuable asset in the field.
Network Administrator
A Network Administrator designs, builds, and maintains computer networks. This course in Siamese Network with Triplet Loss in Keras may be useful for aspiring Network Administrators as it provides a foundation in the principles of network design and management, making it a valuable asset in the field.
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and systems. This course in Siamese Network with Triplet Loss in Keras may be useful for aspiring Data Engineers as it provides a foundation in the principles of data management and processing, making it a valuable asset in the field.
Systems Administrator
A Systems Administrator designs, builds, and maintains computer systems. This course in Siamese Network with Triplet Loss in Keras may be useful for aspiring Systems Administrators as it provides a foundation in the principles of system design and management, making it a valuable asset in the field.
User Experience Designer
A User Experience Designer designs and evaluates user interfaces for websites and applications. This course in Siamese Network with Triplet Loss in Keras may be useful for aspiring User Experience Designers as it provides a foundation in the principles of human-computer interaction and usability, making it a valuable asset in the field.
Product Manager
A Product Manager develops and manages products and services. This course in Siamese Network with Triplet Loss in Keras may be useful for aspiring Product Managers as it provides a foundation in the principles of product management and development, making it a valuable asset in the field.

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 Siamese Network with Triplet Loss in Keras.
Practical guide to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preprocessing, model building, and training. This book valuable resource for anyone who wants to learn more about machine learning with Scikit-Learn, Keras, and TensorFlow.
Provides a comprehensive overview of machine learning with TensorFlow. It covers the basics of machine learning, such as supervised learning and unsupervised learning, as well as more advanced topics, such as deep learning and reinforcement learning. This book valuable resource for anyone who wants to learn more about machine learning with TensorFlow.
Comprehensive guide to machine learning with Python. It covers the basics of machine learning, such as supervised learning and unsupervised learning, as well as more advanced topics, such as deep learning and reinforcement learning. This book valuable resource for anyone who wants to learn more about machine learning with Python.
Comprehensive guide to deep learning with Python. It covers the basics of deep learning, such as neural networks and convolutional neural networks, as well as more advanced topics, such as recurrent neural networks and generative adversarial networks. This book valuable resource for anyone who wants to learn more about deep learning.
Gentle introduction to machine learning. It covers the basics of machine learning, such as supervised learning and unsupervised learning, in a clear and concise way. This book valuable resource for anyone who wants to learn more about machine learning without getting bogged down in the details.
Practical guide to TensorFlow 2.0. It covers a wide range of topics, including data preprocessing, model building, and training. This book valuable resource for anyone who wants to learn more about TensorFlow 2.0.

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