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Andrew Ng,
Kian Katanforoosh,
and
Younes Bensouda Mourri
يعلمك هذا البرنامج "سحر" الحصول على التعلم المتعمق للعمل بشكل جيد. وعوضًا عن كون عملية التعلم المتعمق عبارة عن صندوق أسود، ستدرك الأمر الذي يدفع إلى الأداء، ومن ثم ستتمكن من الحصول على نتائج جيدة بشكل أكثر منهجية. كما ستعرف عن برنامج تنسرفلو.
بعد 3 أسابيع،:
- ستفهم أفضل الممارسات في المجال بشأن إنشاء تطبيقات التعلم المتعمق.
- وأيضًا القدرة على استخدام "الحيل" الشائعة للشبكة العصبية بشكل فعال، بما في ذلك التهيئة، المستوى الثاني وتصحيح وضع التسرب، تطبيع الدفعة، فحص التدرج،,
Read more
يعلمك هذا البرنامج "سحر" الحصول على التعلم المتعمق للعمل بشكل جيد. وعوضًا عن كون عملية التعلم المتعمق عبارة عن صندوق أسود، ستدرك الأمر الذي يدفع إلى الأداء، ومن ثم ستتمكن من الحصول على نتائج جيدة بشكل أكثر منهجية. كما ستعرف عن برنامج تنسرفلو.
بعد 3 أسابيع،:
- ستفهم أفضل الممارسات في المجال بشأن إنشاء تطبيقات التعلم المتعمق.
- وأيضًا القدرة على استخدام "الحيل" الشائعة للشبكة العصبية بشكل فعال، بما في ذلك التهيئة، المستوى الثاني وتصحيح وضع التسرب، تطبيع الدفعة، فحص التدرج،,
Read more
يعلمك هذا البرنامج "سحر" الحصول على التعلم المتعمق للعمل بشكل جيد. وعوضًا عن كون عملية التعلم المتعمق عبارة عن صندوق أسود، ستدرك الأمر الذي يدفع إلى الأداء، ومن ثم ستتمكن من الحصول على نتائج جيدة بشكل أكثر منهجية. كما ستعرف عن برنامج تنسرفلو.
بعد 3 أسابيع،:
- ستفهم أفضل الممارسات في المجال بشأن إنشاء تطبيقات التعلم المتعمق.
- وأيضًا القدرة على استخدام "الحيل" الشائعة للشبكة العصبية بشكل فعال، بما في ذلك التهيئة، المستوى الثاني وتصحيح وضع التسرب، تطبيع الدفعة، فحص التدرج،,
- القدرة على تنفيذ وتطبيق مجموعة متنوعة من خوارزميات التحسين، مثل هبوط تدرج الدفعات الصغيرة وزخم الحركة وطرق المعدل المقترحة وآدم والتحقق من التطابق فيما بينهما.
- بالإضافة إلى فهم أفضل الممارسات الجديدة لعصر التعلم المتعمق حول كيفية إعداد مجموعات التدريب/التطوير/الاختبار وتحليل الانحياز/التباين
- القدرة على تطبيق شبكة عصبية في برنامج تنسرفلو.
هذا هو المساق الثاني من اختصاص التعلم المتعمق.
Register for this course and see more details by visiting:
OpenCourser.com/course/ug80o8/t`zyz
What's inside
Syllabus
الجوانب العملية للتعلم المتعمق
خوارزميات التحسين
الأطر الخاصة بضبط مقياس فرط المعلمات وبرمجة وتطبيع الدفعات
Read more
Syllabus
الجوانب العملية للتعلم المتعمق
خوارزميات التحسين
الأطر الخاصة بضبط مقياس فرط المعلمات وبرمجة وتطبيع الدفعات
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Combines theoretical underpinnings with practical application of deep learning
Suitable for learners with an existing grasp of deep learning concepts
Provides opportunities to explore experimental techniques and methods
Save this course
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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 تعزيز الشبكات العصبية : ضبط وتحسين مقياس فرط المعلمات with these
activities:
Review linear algebra concepts
Show steps
Strengthen your foundation in linear algebra to improve your understanding of deep learning algorithms.
Browse courses on
Linear Algebra
Show steps
-
Go over your linear algebra notes or textbooks.
-
Solve practice problems related to topics like matrices, vectors, and transformations.
Review linear algebra
Show steps
Refreshes your understanding of matrix operations, vector spaces, and other linear algebra concepts, which are foundational to this course.
Browse courses on
Linear Algebra
Show steps
-
Review matrix operations (addition, subtraction, multiplication)
-
Review vector spaces (linear independence, span, basis)
-
Review eigenvalues and eigenvectors
Organize a study group with peers
Show steps
Enhance your learning by collaborating with peers to discuss concepts, solve problems, and share resources.
Browse courses on
Deep Learning
Show steps
-
Identify a group of peers with similar learning goals.
-
Set up regular study sessions.
-
Take turns leading discussions, presenting concepts, and solving problems.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Gather resources on deep learning best practices
Show steps
Enhance your understanding of deep learning by compiling a collection of articles, tutorials, and code examples that cover best practices.
Browse courses on
Deep Learning
Show steps
-
Conduct research to identify reputable sources on deep learning best practices.
-
Organize the resources into a central location, such as a digital notebook or online repository.
-
Review the resources regularly and update them as needed.
Follow tutorials on advanced TensorFlow techniques
Show steps
Expand your TensorFlow skills by following tutorials that cover advanced techniques and best practices.
Browse courses on
TensorFlow
Show steps
-
Identify areas where you want to improve your TensorFlow skills.
-
Find reputable tutorials or online courses that cover these topics.
-
Follow the tutorials and complete the exercises.
-
Experiment with the techniques you learn on your own projects.
Create a neural network diagram using TensorFlow
Show steps
Helps you understand the architecture and functionality of neural networks, a core concept in this course.
Browse courses on
TensorFlow
Show steps
-
Choose a neural network architecture (e.g., CNN, RNN)
-
Implement the network using TensorFlow
-
Visualize the network's layers and connections
Attend workshops on specialized deep learning topics
Show steps
Gain exposure to cutting-edge research and industry trends in deep learning by attending specialized workshops.
Browse courses on
Deep Learning
Show steps
-
Identify workshops that align with your interests and learning goals.
-
Register and attend the workshops.
-
Actively participate in the discussions and exercises.
-
Follow up with the workshop organizers or speakers to continue learning.
Work through TensorFlow exercises
Show steps
Reinforce your understanding of TensorFlow's functionality and syntax by completing hands-on exercises.
Browse courses on
TensorFlow
Show steps
-
Find online exercises or tutorials that cover TensorFlow basics.
-
Set up your TensorFlow environment and run through the exercises.
-
Experiment with different TensorFlow functions and modules.
Build a neural network model in TensorFlow
Show steps
Apply your knowledge of TensorFlow to create a practical neural network model that can solve a specific problem.
Browse courses on
Neural Networks
Show steps
-
Identify a problem that can be solved using a neural network.
-
Design and implement the neural network model in TensorFlow.
-
Train and evaluate the model on a dataset.
-
Deploy and use the model to solve the problem.
Mentor junior students or colleagues in deep learning
Show steps
Solidify your understanding of deep learning by sharing your knowledge and helping others learn.
Browse courses on
Deep Learning
Show steps
-
Identify opportunities to mentor junior students or colleagues.
-
Prepare materials and resources to support your mentees' learning.
-
Regularly meet with your mentees to provide guidance and feedback.
Review linear algebra concepts
Show steps
Strengthen your foundation in linear algebra to improve your understanding of deep learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Go over your linear algebra notes or textbooks.
- Solve practice problems related to topics like matrices, vectors, and transformations.
Review linear algebra
Show steps
Refreshes your understanding of matrix operations, vector spaces, and other linear algebra concepts, which are foundational to this course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review matrix operations (addition, subtraction, multiplication)
- Review vector spaces (linear independence, span, basis)
- Review eigenvalues and eigenvectors
Organize a study group with peers
Show steps
Enhance your learning by collaborating with peers to discuss concepts, solve problems, and share resources.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify a group of peers with similar learning goals.
- Set up regular study sessions.
- Take turns leading discussions, presenting concepts, and solving problems.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Gather resources on deep learning best practices
Show steps
Enhance your understanding of deep learning by compiling a collection of articles, tutorials, and code examples that cover best practices.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Conduct research to identify reputable sources on deep learning best practices.
- Organize the resources into a central location, such as a digital notebook or online repository.
- Review the resources regularly and update them as needed.
Follow tutorials on advanced TensorFlow techniques
Show steps
Expand your TensorFlow skills by following tutorials that cover advanced techniques and best practices.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify areas where you want to improve your TensorFlow skills.
- Find reputable tutorials or online courses that cover these topics.
- Follow the tutorials and complete the exercises.
- Experiment with the techniques you learn on your own projects.
Create a neural network diagram using TensorFlow
Show steps
Helps you understand the architecture and functionality of neural networks, a core concept in this course.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Choose a neural network architecture (e.g., CNN, RNN)
- Implement the network using TensorFlow
- Visualize the network's layers and connections
Attend workshops on specialized deep learning topics
Show steps
Gain exposure to cutting-edge research and industry trends in deep learning by attending specialized workshops.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify workshops that align with your interests and learning goals.
- Register and attend the workshops.
- Actively participate in the discussions and exercises.
- Follow up with the workshop organizers or speakers to continue learning.
Work through TensorFlow exercises
Show steps
Reinforce your understanding of TensorFlow's functionality and syntax by completing hands-on exercises.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find online exercises or tutorials that cover TensorFlow basics.
- Set up your TensorFlow environment and run through the exercises.
- Experiment with different TensorFlow functions and modules.
Build a neural network model in TensorFlow
Show steps
Apply your knowledge of TensorFlow to create a practical neural network model that can solve a specific problem.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Identify a problem that can be solved using a neural network.
- Design and implement the neural network model in TensorFlow.
- Train and evaluate the model on a dataset.
- Deploy and use the model to solve the problem.
Mentor junior students or colleagues in deep learning
Show steps
Solidify your understanding of deep learning by sharing your knowledge and helping others learn.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior students or colleagues.
- Prepare materials and resources to support your mentees' learning.
- Regularly meet with your mentees to provide guidance and feedback.
Career center
Learners who complete تعزيز الشبكات العصبية : ضبط وتحسين مقياس فرط المعلمات will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will design, implement, and maintain machine learning models to solve business problems. To succeed in this role, one should gain a strong foundation of machine learning and its practical applications, such as provided in this course. The concepts covered in this course, including optimization algorithms and model evaluation techniques, are essential for building and deploying successful machine learning models.
Data Scientist
Data Scientist
As a Data Scientist, you will gather, analyze, and interpret data to help organizations make better decisions. To succeed in this role, one requires a strong foundation in statistics, machine learning, and programming. This course provides a comprehensive overview of the practical aspects of machine learning, including model optimization and evaluation techniques, which are crucial skills for Data Scientists.
Software Engineer
Software Engineer
As a Software Engineer, you will design, develop, and maintain software applications. To succeed in this role, one should have a strong foundation in computer science fundamentals and software development practices. This course provides a good overview of practical machine learning techniques, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Software Engineers working on data-driven applications.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you will use mathematical and statistical models to analyze financial data and make investment decisions. To succeed in this role, one requires a strong foundation in statistics, probability, and programming. This course provides a good overview of practical machine learning techniques, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Quantitative Analysts.
Research Scientist
Research Scientist
As a Research Scientist, you will conduct research in various scientific fields, including machine learning. To succeed in this role, one requires a strong foundation in scientific methods and a deep understanding of machine learning algorithms. This course provides a comprehensive overview of practical aspects of machine learning, including model optimization and evaluation techniques, which are essential for Research Scientists working on machine learning projects.
Product Manager
Product Manager
As a Product Manager, you will oversee the development and launch of new products. To succeed in this role, one should have a strong understanding of product development, marketing, and business strategy. This course provides a good overview of practical aspects of machine learning and its applications, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Product Managers working on data-driven products.
Business Analyst
Business Analyst
As a Business Analyst, you will analyze business processes and make recommendations to improve efficiency and profitability. To succeed in this role, one should have a strong understanding of business analysis techniques and data analysis methods. This course provides a good overview of practical aspects of machine learning and its applications, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Business Analysts working on data-driven projects.
Data Engineer
Data Engineer
As a Data Engineer, you will design and manage data pipelines to support data-driven applications. To succeed in this role, one should have a strong foundation in data engineering principles and practices. This course provides a good overview of practical aspects of machine learning and its applications, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Data Engineers working on machine learning projects.
Statistician
Statistician
As a Statistician, you will collect, analyze, and interpret data to help organizations make better decisions. To succeed in this role, one requires a strong foundation in statistics and probability. This course provides a good overview of practical aspects of machine learning and its applications, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Statisticians working on data-driven projects.
Consultant
Consultant
As a Consultant, you will provide expert advice and guidance to clients on a variety of topics. To succeed in this role, one should have a strong understanding of business and industry trends. This course provides a good overview of practical aspects of machine learning and its applications, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Consultants working on data-driven projects.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will analyze financial data and make recommendations to investors. To succeed in this role, one requires a strong foundation in finance and accounting. This course provides a good overview of practical aspects of machine learning and its applications, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Financial Analysts working on data-driven projects.
Project Manager
Project Manager
As a Project Manager, you will plan, execute, and close projects. To succeed in this role, one should have a strong understanding of project management principles and practices. This course provides a good overview of practical aspects of machine learning and its applications, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Project Managers working on data-driven projects.
Operations Research Analyst
Operations Research Analyst
As an Operations Research Analyst, you will use mathematical and statistical models to improve the efficiency of operations. To succeed in this role, one requires a strong foundation in operations research and optimization techniques. This course provides a good overview of practical aspects of machine learning and its applications, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Operations Research Analysts working on data-driven projects.
Market Researcher
Market Researcher
As a Market Researcher, you will conduct research on target markets to help businesses make better decisions. To succeed in this role, one should have a strong understanding of market research methods and data analysis techniques. This course provides a good overview of practical aspects of machine learning and its applications, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Market Researchers working on data-driven projects.
Technical Writer
Technical Writer
As a Technical Writer, you will create documentation and other materials to explain technical concepts. To succeed in this role, one should have a strong understanding of technical writing principles and practices. This course provides a good overview of practical aspects of machine learning and its applications, including optimization algorithms and model evaluation techniques, which are becoming increasingly important for Technical Writers working on data-driven projects.
For more career information including salaries, visit:
OpenCourser.com/course/ug80o8/t`zyz
Reading list
We've selected ten 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
تعزيز الشبكات العصبية : ضبط وتحسين مقياس فرط المعلمات .
Save
Comprehensive guide to deep learning, covering the theoretical foundations and practical applications of this powerful technology. It valuable reference for anyone interested in learning more about deep learning.
Practical guide to using Scikit-Learn, Keras, and TensorFlow, three of the most popular machine learning libraries in Python. It valuable resource for anyone who wants to learn more about these libraries or who is interested in using them for their own projects.
Practical guide to using Keras, a high-level neural networks API, written in Python. It valuable resource for anyone who wants to learn more about Keras or who is interested in using it for their own projects.
Save
Comprehensive introduction to machine learning, covering a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about machine learning.
Comprehensive introduction to pattern recognition and machine learning, covering a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about these topics.
Comprehensive introduction to statistical learning, covering a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about these topics.
Practical guide to using machine learning for hackers, covering a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to using R for machine learning, covering a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to using Python for machine learning, covering a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to using PyTorch for deep learning, covering a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about PyTorch.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ug80o8/t`zyz
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Effort
3 weeks, 3-6 hours per week
Level
Introductory
Via
Coursera
Institution
DeepLearning.AI
Instructors
Andrew Ng
Kian Katanforoosh
Younes Bensouda Mourri
Language
العربية
Transcripts
English
Español
Français
Português
中文
한국어
Pусский
Türkçe
tiếng Việt
Good to know
Combines theoretical underpinnings with practical application of deep learning
Suitable for learners with an existing grasp of deep learning concepts
Provides opportunities to explore experimental techniques and methods
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