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בקורס נלמד על מנגנון תשומת הלב, שיטה טובה מאוד שמאפשרת לרשתות נוירונים להתמקד בחלקים ספציפיים ברצף הקלט. נלמד איך עובד העיקרון של תשומת הלב, ואיך אפשר להשתמש בו כדי לשפר את הביצועים במגוון משימות של למידת מכונה, כולל תרגום אוטומטי, סיכום טקסט ומענה לשאלות.
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What's inside
Syllabus
מבוא למנגנון תשומת הלב
ביחידה הזו נלמד איך עובד העיקרון של תשומת הלב, ואיך אפשר להשתמש בו כדי לשפר את הביצועים במגוון משימות של למידת מכונה, כולל תרגום אוטומטי, סיכום טקסט ומענה לשאלות.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Google Cloud Training, who are experts in their field
Helps learners develop skills in automated translation, text summarization, and question-answering
Makes use of attention mechanisms in neural networks to improve performance in machine learning tasks
Save this course
Save Attention Mechanism - בעברית 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 Attention Mechanism - בעברית with these
activities:
Read 'Neural Networks and Deep Learning'
Show steps
This book provides a mathematical introduction to neural networks and deep learning, covering topics such as backpropagation, convolutional neural networks, and recurrent neural networks.
Show steps
-
Read the first 10 chapters of the book
-
Complete the exercises and programming assignments
-
Summarize the main concepts of each chapter
Attend a deep learning study group
Show steps
This group will provide you with an opportunity to discuss deep learning concepts with other students and get feedback on your work.
Show steps
-
Find a study group or start one yourself
-
Prepare for the study group by reading the assigned materials
-
Participate in the discussion and share your own insights
Follow the Coursera Deep Learning Specialization
Show steps
This specialization provides a comprehensive introduction to deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Browse courses on
Deep Learning
Show steps
-
Complete the first three courses in the specialization
-
Complete the programming assignments and projects
-
Participate in the discussion forums
Six other activities
Expand to see all activities and additional details
Show all nine activities
Mentor a junior student in deep learning
Show steps
This activity will help you solidify your understanding of deep learning by teaching it to others.
Show steps
-
Find a junior student to mentor
-
Create a study plan and meet regularly
-
Provide guidance and feedback on the student's work
Solve deep learning coding problems on LeetCode
Show steps
This activity will help you develop your deep learning coding skills by solving problems on a platform like LeetCode.
Show steps
-
Create an account on LeetCode
-
Solve the easy and medium-level deep learning problems
-
Discuss your solutions with other users in the forums
תרגלו ביישום מנגנון תשומת הלב
Show steps
תרגול ביישום מנגנון תשומת הלב יעזור לכם לחזק את הבנתכם כיצד הוא עובד ולשפר את ביצועיכם במגוון משימות למידת מכונה.
Show steps
-
מצאו קבוצת נתונים מתאימה והכינו את הנתונים שלכם.
-
יישמו את מנגנון תשומת הלב ברשת נוירונית.
-
אמצו את הרשת על קבוצת הנתונים המוכנה שלכם.
-
הערכו את ביצועי הרשת על קבוצת הנתונים המאומתת שלכם.
Build a simple neural network from scratch
Show steps
This project will help you understand the inner workings of neural networks by building one from scratch using a library like TensorFlow or PyTorch.
Show steps
-
Design the architecture of the neural network
-
Implement the forward and backward pass
-
Train the neural network on a simple dataset
Contribute to an open-source deep learning project
Show steps
This activity will help you learn about the latest deep learning techniques and contribute to the open-source community.
Show steps
-
Find an open-source deep learning project
-
Identify a way to contribute
-
Submit a pull request
Write a research proposal on a deep learning topic
Show steps
This activity will help you develop your research skills and demonstrate your understanding of a deep learning topic.
Show steps
-
Choose a topic and conduct a literature review
-
Develop a research question and hypothesis
-
Design a research methodology
Read 'Neural Networks and Deep Learning'
Show steps
This book provides a mathematical introduction to neural networks and deep learning, covering topics such as backpropagation, convolutional neural networks, and recurrent neural networks.
Show steps
Show steps
Show steps
- Read the first 10 chapters of the book
- Complete the exercises and programming assignments
- Summarize the main concepts of each chapter
Attend a deep learning study group
Show steps
This group will provide you with an opportunity to discuss deep learning concepts with other students and get feedback on your work.
Show steps
Show steps
Show steps
- Find a study group or start one yourself
- Prepare for the study group by reading the assigned materials
- Participate in the discussion and share your own insights
Follow the Coursera Deep Learning Specialization
Show steps
This specialization provides a comprehensive introduction to deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Complete the first three courses in the specialization
- Complete the programming assignments and projects
- Participate in the discussion forums
Six other activities
Expand to see all activities and additional details
Show all nine activities
Mentor a junior student in deep learning
Show steps
This activity will help you solidify your understanding of deep learning by teaching it to others.
Show steps
Show steps
Show steps
- Find a junior student to mentor
- Create a study plan and meet regularly
- Provide guidance and feedback on the student's work
Solve deep learning coding problems on LeetCode
Show steps
This activity will help you develop your deep learning coding skills by solving problems on a platform like LeetCode.
Show steps
Show steps
Show steps
- Create an account on LeetCode
- Solve the easy and medium-level deep learning problems
- Discuss your solutions with other users in the forums
תרגלו ביישום מנגנון תשומת הלב
Show steps
תרגול ביישום מנגנון תשומת הלב יעזור לכם לחזק את הבנתכם כיצד הוא עובד ולשפר את ביצועיכם במגוון משימות למידת מכונה.
Show steps
Show steps
Show steps
- מצאו קבוצת נתונים מתאימה והכינו את הנתונים שלכם.
- יישמו את מנגנון תשומת הלב ברשת נוירונית.
- אמצו את הרשת על קבוצת הנתונים המוכנה שלכם.
- הערכו את ביצועי הרשת על קבוצת הנתונים המאומתת שלכם.
Build a simple neural network from scratch
Show steps
This project will help you understand the inner workings of neural networks by building one from scratch using a library like TensorFlow or PyTorch.
Show steps
Show steps
Show steps
- Design the architecture of the neural network
- Implement the forward and backward pass
- Train the neural network on a simple dataset
Contribute to an open-source deep learning project
Show steps
This activity will help you learn about the latest deep learning techniques and contribute to the open-source community.
Show steps
Show steps
Show steps
- Find an open-source deep learning project
- Identify a way to contribute
- Submit a pull request
Write a research proposal on a deep learning topic
Show steps
This activity will help you develop your research skills and demonstrate your understanding of a deep learning topic.
Show steps
Show steps
Show steps
- Choose a topic and conduct a literature review
- Develop a research question and hypothesis
- Design a research methodology
Career center
Learners who complete Attention Mechanism - בעברית will develop knowledge and skills
that may be useful to these careers:
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be helpful to an aspiring Product Manager because it teaches the principles of machine learning, which can be used to develop new products.
Consultant
Consultant
Consultants help businesses solve problems and make better decisions. This course may be helpful to an aspiring Consultant because it teaches the principles of machine learning, which is a valuable tool for solving problems and making decisions.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software systems. This course may be useful to an aspiring Software Engineer because it provides exposure to the field of machine learning and its applications through the study of the Attention Mechanism.
Marketing Analyst
Marketing Analyst
Marketing Analysts help businesses understand their customers and make better decisions. This course may be useful to a future Marketing Analyst because it provides a foundation in the principles of machine learning.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to help businesses make better decisions. This course provides a good foundation in the principles of machine learning, which can be applied to operations research.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to help businesses make better decisions. This course may be useful to an aspiring Financial Analyst because it provides a foundation in the principles of machine learning.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks. This course may be useful to a future Risk Analyst because it provides a foundation in the principles of machine learning.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. This course may be helpful to an aspiring Quantitative Analyst because it introduces the principles of machine learning, which is a field closely related to quantitative analysis.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course may be useful to a future Actuary because it provides a foundation in the principles of machine learning.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data to uncover insights and trends. This course may be useful to a future Data Scientist because it will help them understand how to work with and analyze data.
Underwriter
Underwriter
Underwriters assess and manage risk for insurance companies. This course may be useful to a future Underwriter because it provides a foundation in the principles of machine learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers apply principles of machine learning to help design, build, deploy, and manage machine learning systems. This course may be useful to an aspiring Machine Learning Engineer because it provides a good foundation in the principles of machine learning.
Business Analyst
Business Analyst
Business Analysts help businesses understand their data and make better decisions. This course may be useful to a future Business Analyst because it provides a foundation in the principles of machine learning.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course may be useful to an aspiring Statistician because it provides a foundation in the principles of machine learning.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. This course may be useful to a future Data Analyst because it provides a foundation in the principles of machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/k78vk4/attention
Reading list
We've selected nine 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
Attention Mechanism - בעברית.
Save
Provides a comprehensive overview of deep learning techniques applied to natural language processing. It covers a wide range of topics relevant to the course and can serve as a valuable reference resource.
A comprehensive overview of deep learning for NLP, including a chapter on attention mechanisms.
Offers a rigorous and theoretical treatment of machine learning, providing a solid foundation for understanding attention mechanisms. It valuable resource for those interested in the mathematical and algorithmic aspects of attention.
Provides a practical guide to deep learning using the Python programming language. It includes hands-on examples and exercises that can enhance the understanding of the concepts covered in the course.
Save
Provides a comprehensive overview of deep learning, including a chapter on attention mechanisms.
Provides a comprehensive overview of NLP with Python, including a section on attention mechanisms.
Provides a comprehensive overview of NLP, including a section on attention mechanisms.
Provides a practical guide to deep learning with Python, including a section on attention mechanisms.
A practical guide to machine learning with Python, including a section on attention mechanisms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/k78vk4/attention
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Effort
1 week of study, 6-8 hours
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
עברית
Transcript
English
Good to know
Taught by Google Cloud Training, who are experts in their field
Helps learners develop skills in automated translation, text summarization, and question-answering
Makes use of attention mechanisms in neural networks to improve performance in machine learning tasks
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