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Leire Ahedo
En muchos casos cuando queremos entrenar modelos de clasificación para predecir una clase minoritaria, no es fácil obtener datos de esta clase. En este curso aprenderás a entrenar modelos capaces de predecir estas clases minoritarias aún sin datos.
Por ello, en este curso te enseñaremos a entrenar y utilizar Autoencoders para procesar los datos existentes y que las clases sean más fácilmente distinguibles. También te enseñaremos a utilizar los propios Autoencoders para predecir la clase minoritaria en eventos extremadamente infrecuentes o cuando no tenemos datos de esta clase.
Register for this course and see more details by visiting:
OpenCourser.com/course/uc23my/autoencoders
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops Autoencoder models, which are critical for effective minority class prediction in imbalanced datasets
Provides hands-on experience with Autoencoders, fostering practical skills in data processing and model building
Facilitates the prediction of minority classes, addressing a common challenge in imbalanced data classification
Suitable for learners with basic experience in machine learning and data analysis
Save this course
Save Autoencoders y eventos extremadamente infrecuentes 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 Autoencoders y eventos extremadamente infrecuentes with these
activities:
Review Autoencoder Basics
Show steps
Improve understanding of Autoencoders and Neural Networks, creating a strong foundation for the course.
Browse courses on
Autoencoders
Show steps
-
Revisit the basics of Neural Networks.
-
Explore different types of Autoencoders.
-
Implement a simple Autoencoder in a programming language.
Create a Comprehensive Study Guide
Show steps
Improve retention and recall by organizing notes, assignments, and other materials into a cohesive study resource.
Browse courses on
Study Guide
Show steps
-
Gather all relevant course materials.
-
Review and summarize key concepts, definitions, and examples.
-
Create a structured outline or mind map to organize the information.
-
Add visual aids, such as diagrams or charts, to enhance understanding.
Create a Visual Guide to Autoencoder Architecture
Show steps
Solidify understanding of Autoencoder architecture by creating a visual representation, enhancing conceptual clarity.
Show steps
-
Gather information about Autoencoder architecture.
-
Design a visual representation using a tool like draw.io or Lucidchart.
-
Label and annotate the diagram to explain the components and flow of the Autoencoder.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Guided Tutorials on Autoencoder Implementations
Show steps
Gain practical experience implementing Autoencoders using popular frameworks, reinforcing course concepts.
Browse courses on
TensorFlow
Show steps
-
Identify online tutorials for implementing Autoencoders.
-
Choose a framework (e.g., TensorFlow, Keras) and follow the tutorial.
-
Experiment with different Autoencoder architectures and parameters.
Solve Practice Problems on Autoencoder Training
Show steps
Develop problem-solving skills and deepen understanding of Autoencoder training techniques, enhancing comprehension of course material.
Browse courses on
Loss Functions
Show steps
-
Locate practice problems related to Autoencoder training.
-
Attempt to solve the problems using course knowledge.
-
Analyze solutions and identify areas for improvement.
-
Experiment with different training parameters and observe their impact on Autoencoder performance.
Attend a Workshop on Autoencoder Applications
Show steps
Gain insights into practical applications of Autoencoders, broadening knowledge and understanding.
Browse courses on
Image Processing
Show steps
-
Identify relevant workshops on Autoencoder applications.
-
Register and attend the workshop.
-
Actively participate in discussions and ask questions to clarify concepts.
-
Connect with experts and practitioners in the field.
Participate in an Autoencoder Competition
Show steps
Challenge oneself, test skills, and gain valuable experience in applying Autoencoders to real-world problems.
Browse courses on
Kaggle
Show steps
-
Identify and register for an Autoencoder competition.
-
Develop and implement an Autoencoder solution.
-
Submit the solution and track progress on the leaderboard.
-
Analyze results and identify areas for improvement.
Review Autoencoder Basics
Show steps
Improve understanding of Autoencoders and Neural Networks, creating a strong foundation for the course.
Browse courses on
Autoencoders
Show steps
Show steps
Show steps
- Revisit the basics of Neural Networks.
- Explore different types of Autoencoders.
- Implement a simple Autoencoder in a programming language.
Create a Comprehensive Study Guide
Show steps
Improve retention and recall by organizing notes, assignments, and other materials into a cohesive study resource.
Browse courses on
Study Guide
Show steps
Show steps
Show steps
- Gather all relevant course materials.
- Review and summarize key concepts, definitions, and examples.
- Create a structured outline or mind map to organize the information.
- Add visual aids, such as diagrams or charts, to enhance understanding.
Create a Visual Guide to Autoencoder Architecture
Show steps
Solidify understanding of Autoencoder architecture by creating a visual representation, enhancing conceptual clarity.
Show steps
Show steps
Show steps
- Gather information about Autoencoder architecture.
- Design a visual representation using a tool like draw.io or Lucidchart.
- Label and annotate the diagram to explain the components and flow of the Autoencoder.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Guided Tutorials on Autoencoder Implementations
Show steps
Gain practical experience implementing Autoencoders using popular frameworks, reinforcing course concepts.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify online tutorials for implementing Autoencoders.
- Choose a framework (e.g., TensorFlow, Keras) and follow the tutorial.
- Experiment with different Autoencoder architectures and parameters.
Solve Practice Problems on Autoencoder Training
Show steps
Develop problem-solving skills and deepen understanding of Autoencoder training techniques, enhancing comprehension of course material.
Browse courses on
Loss Functions
Show steps
Show steps
Show steps
- Locate practice problems related to Autoencoder training.
- Attempt to solve the problems using course knowledge.
- Analyze solutions and identify areas for improvement.
- Experiment with different training parameters and observe their impact on Autoencoder performance.
Attend a Workshop on Autoencoder Applications
Show steps
Gain insights into practical applications of Autoencoders, broadening knowledge and understanding.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Identify relevant workshops on Autoencoder applications.
- Register and attend the workshop.
- Actively participate in discussions and ask questions to clarify concepts.
- Connect with experts and practitioners in the field.
Participate in an Autoencoder Competition
Show steps
Challenge oneself, test skills, and gain valuable experience in applying Autoencoders to real-world problems.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Identify and register for an Autoencoder competition.
- Develop and implement an Autoencoder solution.
- Submit the solution and track progress on the leaderboard.
- Analyze results and identify areas for improvement.
Career center
Learners who complete Autoencoders y eventos extremadamente infrecuentes will develop knowledge and skills
that may be useful to these careers:
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, such as computer science, engineering, and medicine. This course will provide you with the foundation in machine learning and anomaly detection, which can be applied to a variety of research problems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course will provide you with the foundation in machine learning and anomaly detection, which can be applied to a variety of quantitative finance applications, such as risk management, trading, and portfolio optimization.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and deploying machine learning models. The techniques taught in this course, including autoencoders and anomaly detection, can be applied to a variety of machine learning tasks, such as fraud detection, predictive maintenance, and personalized recommendations.
Fraud Analyst
Fraud Analyst
Fraud Analysts investigate and prevent fraud. The ability to identify rare events and make predictions without data can be a valuable asset in this role, as it can help businesses detect and prevent fraudulent activities.
Data Scientist
Data Scientist
As a Data Scientist, you will leverage large datasets to help inform critical business decisions. This course will help build a foundation in autoencoders, which will enable you to preprocess data and extract meaningful features. The ability to accurately classify rare events and make predictions without data can be a valuable asset in fields such as healthcare, finance, and manufacturing.
Data Architect
Data Architect
Data Architects design and manage data systems. This course will provide you with the foundation in data preprocessing and anomaly detection, which can help you build data systems that are reliable, efficient, and scalable.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks to an organization. This course will provide you with anomaly detection techniques, such as autoencoders, to help you identify rare events and mitigate their potential impact.
Professor
Professor
Professors teach and conduct research in a variety of academic disciplines. This course will provide you with the foundation in machine learning and anomaly detection, which can be applied to a variety of research and teaching topics.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. The ability to identify rare events and make predictions without data can be a valuable asset in this role, as it can help actuaries develop more accurate and reliable risk models.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course will provide you with the foundation in data preprocessing and anomaly detection, which can help you ensure that databases are reliable, efficient, and secure.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. The techniques taught in this course, including autoencoders and anomaly detection, can be applied to a variety of software engineering tasks, such as software testing, debugging, and performance optimization.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to solve business problems and improve decision-making. The ability to preprocess data and identify rare events can be a valuable asset in this role, as it can help businesses identify opportunities and mitigate risks.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. This course will provide you with the foundation in machine learning and anomaly detection, which can help you write clear and concise documentation for complex technical topics.
Consultant
Consultant
Consultants provide advice and guidance to businesses and organizations. This course will provide you with the foundation in machine learning and anomaly detection, which can help you develop and deliver effective consulting solutions.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course will provide you with data preprocessing techniques, including autoencoders, to handle large and complex datasets effectively.
For more career information including salaries, visit:
OpenCourser.com/course/uc23my/autoencoders
Reading list
We've selected 12 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
Autoencoders y eventos extremadamente infrecuentes.
This paper introduces variational autoencoders, a type of autoencoder that can be used to generate new data. It valuable resource for anyone who wants to learn more about variational autoencoders and their applications.
Provides a comprehensive overview of manifold learning, a technique for reducing the dimensionality of data. It valuable resource for anyone who wants to learn more about manifold learning and its applications.
Provides a comprehensive overview of dimensionality reduction techniques for machine learning. It valuable resource for anyone who wants to learn more about dimensionality reduction and its applications.
Provides a comprehensive overview of statistical learning methods for sparse data. It valuable resource for anyone who wants to learn more about statistical learning and its applications to sparse data.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It valuable resource for anyone who wants to learn more about these topics and their applications.
Save
Provides a comprehensive overview of machine learning. It valuable resource for anyone who wants to learn more about machine learning and its applications.
Save
Provides a comprehensive overview of deep learning. It valuable resource for anyone who wants to learn more about deep learning and its applications.
Provides a comprehensive overview of reinforcement learning. It valuable resource for anyone who wants to learn more about reinforcement learning and its applications.
Provides a comprehensive overview of natural language processing with Python. It valuable resource for anyone who wants to learn more about natural language processing and its applications.
Provides a comprehensive overview of data science from scratch. It valuable resource for anyone who wants to learn more about data science and its applications.
Provides a comprehensive introduction to deep learning, covering the basics of neural networks, convolutional neural networks, recurrent neural networks, and more. It valuable resource for anyone who wants to learn more about deep learning and its applications.
Save
Provides a practical guide to data science. It valuable resource for anyone who wants to learn more about data science and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uc23my/autoencoders
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Good to know
Develops Autoencoder models, which are critical for effective minority class prediction in imbalanced datasets
Provides hands-on experience with Autoencoders, fostering practical skills in data processing and model building
Facilitates the prediction of minority classes, addressing a common challenge in imbalanced data classification
Suitable for learners with basic experience in machine learning and data analysis
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