We may earn an affiliate commission when you visit our partners.
Alper Tellioglu
Unlock the power of pre-trained models. This course will teach you how to use and fine-tune pre-trained models for a range of applications, including natural language processing and image recognition.
Read more
Unlock the power of pre-trained models. This course will teach you how to use and fine-tune pre-trained models for a range of applications, including natural language processing and image recognition.
Read more
Unlock the power of pre-trained models. This course will teach you how to use and fine-tune pre-trained models for a range of applications, including natural language processing and image recognition.
In the evolving environment of artificial intelligence, harnessing the power of pre-trained models is becoming increasingly crucial for efficient and effective solutions.
In this course, Applying Neural Networks: A Guide to Pre-trained Models, you'll gain the ability to use existing AI models to accelerate your projects.
First, you’ll explore the world of pre-trained models and understand their significance.
Next, you’ll discover how to find the most popular models, and how to integrate them.
Finally, you’ll learn how to adapt these models to your specific needs, exploring techniques for fine-tuning and transfer learning.
When you’re finished with this course, you’ll have the skills and knowledge of pre-trained models needed to significantly improve your project results.
Register for this course and see more details by visiting:
OpenCourser.com/course/fn82l5/applying
Here's a deal for you
We found an offer that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
What's inside
Syllabus
Course Overview
Understanding Pre-trained Models
Fine-Tuning, Evaluation, and Practical Insights
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces essential concepts of pre-trained models
Offers a practical understanding of pre-trained models through hands-on applications
Facilitates the integration of pre-trained models into existing projects
Provides a comprehensive overview of pre-trained models and their real-world applications
Covers advanced techniques such as fine-tuning and transfer learning for customizing pre-trained models
Suitable for learners with some background in machine learning and artificial intelligence
Save this course
Save Applying Neural Networks: A Guide to Pre-trained Models to your list so you can find it easily later:
Save
Save
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 Applying Neural Networks: A Guide to Pre-trained Models with these
activities:
Read "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Gain a comprehensive understanding of the foundations and applications of deep learning, providing a solid basis for working with pre-trained models.
View
Deep Learning
on Amazon
Show steps
-
Read and understand the chapters on supervised learning and neural networks.
-
Work through the exercises and examples provided in the book.
Review Linear Algebra
Show steps
Reinforce your understanding of matrix operations and vector spaces, which are frequently used with pre-trained models.
Browse courses on
Linear Algebra
Show steps
-
Review the concepts of matrices, vectors, and vector spaces.
-
Practice solving systems of linear equations.
-
Review the basics of eigenvalues and eigenvectors.
Connect with Experts in Pre-trained Model Development
Show steps
Expand your knowledge and gain valuable insights by connecting with professionals who specialize in pre-trained model development.
Show steps
-
Attend industry events or online conferences focused on pre-trained models.
-
Reach out to researchers or practitioners in the field through LinkedIn or email.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve Pre-trained Model Practice Problems
Show steps
Strengthen your ability to use and manipulate pre-trained models by solving practice problems, improving your problem-solving skills.
Show steps
-
Find online resources or textbooks with practice problems on pre-trained models.
-
Solve the problems, focusing on understanding the underlying concepts and techniques.
-
Review your solutions and identify areas for improvement.
Build a Simple NLP Model Using a Pre-trained Language Model
Show steps
Apply your knowledge of pre-trained models by building a practical NLP application, reinforcing the concepts and techniques.
Show steps
-
Choose a pre-trained language model such as BERT or GPT-3.
-
Fine-tune the model on a specific NLP task, such as text classification or sentiment analysis.
-
Evaluate the performance of your model and make adjustments as needed.
Develop a Project Using a Pre-trained Image Recognition Model
Show steps
Integrate pre-trained models into a real-world application by developing a project, enhancing your understanding of their practical implementation.
Show steps
-
Identify a problem or task that can be solved using image recognition.
-
Select a pre-trained image recognition model and fine-tune it for your specific task.
-
Build an application that incorporates the model and evaluates its performance.
Present Your Project or Findings on Pre-trained Models
Show steps
Solidify your understanding and demonstrate your skills by presenting your project or findings to others, receiving valuable feedback and expanding your knowledge.
Show steps
-
Prepare a presentation that showcases your project or research on pre-trained models.
-
Present your work at a conference, meet-up, or to your peers for feedback.
Read "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Gain a comprehensive understanding of the foundations and applications of deep learning, providing a solid basis for working with pre-trained models.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read and understand the chapters on supervised learning and neural networks.
- Work through the exercises and examples provided in the book.
Review Linear Algebra
Show steps
Reinforce your understanding of matrix operations and vector spaces, which are frequently used with pre-trained models.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the concepts of matrices, vectors, and vector spaces.
- Practice solving systems of linear equations.
- Review the basics of eigenvalues and eigenvectors.
Connect with Experts in Pre-trained Model Development
Show steps
Expand your knowledge and gain valuable insights by connecting with professionals who specialize in pre-trained model development.
Show steps
Show steps
Show steps
- Attend industry events or online conferences focused on pre-trained models.
- Reach out to researchers or practitioners in the field through LinkedIn or email.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve Pre-trained Model Practice Problems
Show steps
Strengthen your ability to use and manipulate pre-trained models by solving practice problems, improving your problem-solving skills.
Show steps
Show steps
Show steps
- Find online resources or textbooks with practice problems on pre-trained models.
- Solve the problems, focusing on understanding the underlying concepts and techniques.
- Review your solutions and identify areas for improvement.
Build a Simple NLP Model Using a Pre-trained Language Model
Show steps
Apply your knowledge of pre-trained models by building a practical NLP application, reinforcing the concepts and techniques.
Show steps
Show steps
Show steps
- Choose a pre-trained language model such as BERT or GPT-3.
- Fine-tune the model on a specific NLP task, such as text classification or sentiment analysis.
- Evaluate the performance of your model and make adjustments as needed.
Develop a Project Using a Pre-trained Image Recognition Model
Show steps
Integrate pre-trained models into a real-world application by developing a project, enhancing your understanding of their practical implementation.
Show steps
Show steps
Show steps
- Identify a problem or task that can be solved using image recognition.
- Select a pre-trained image recognition model and fine-tune it for your specific task.
- Build an application that incorporates the model and evaluates its performance.
Present Your Project or Findings on Pre-trained Models
Show steps
Solidify your understanding and demonstrate your skills by presenting your project or findings to others, receiving valuable feedback and expanding your knowledge.
Show steps
Show steps
Show steps
- Prepare a presentation that showcases your project or research on pre-trained models.
- Present your work at a conference, meet-up, or to your peers for feedback.
Career center
Learners who complete Applying Neural Networks: A Guide to Pre-trained Models will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and maintain machine learning models. As an ML Engineer, you'd use pre-trained models, such as the ones that you will study in this course, to build and improve machine learning solutions. Applying Neural Networks: A Guide to Pre-trained Models can be a useful addition to your training as it will teach the concepts, patterns, and best practices around pre-trained models.
Data Scientist
Data Scientist
Data Scientists use data and machine learning to solve business problems. In this role, you will benefit from understanding pre-trained models. Understanding how to use and fine-tune pre-trained models will help you build better and more powerful machine learning solutions. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your comprehension of pre-trained models.
Software Engineer
Software Engineer
A Software Engineer builds and maintains software systems. In this role, you may use pre-trained models to improve the functionality of the software systems that you build. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it can provide you with an understanding of pre-trained models and their applications.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts develop and use mathematical and statistical models to analyze data and make predictions. Leveraging pre-trained models in this field can accelerate and enhance these processes. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your understanding of pre-trained models and their applications.
Product Manager
Product Manager
Product Managers plan and oversee the development of products. Incorporating pre-trained models into products is a growing trend that can give your products an edge over the competition. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your understanding of pre-trained models and their applications.
Business Analyst
Business Analyst
Business Analysts gather and analyze data to help businesses make better decisions. Incorporating pre-trained models into your analysis can lead to more accurate and insightful results. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your understanding of pre-trained models and their applications.
Consultant
Consultant
Consultants provide advice and expertise to businesses and organizations. Adding knowledge of pre-trained models to your skillset can increase your value to potential clients. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will provide you with a comprehensive understanding of pre-trained models and their applications.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. Incorporating pre-trained models into your work can increase the efficiency and effectiveness of your analyses. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your understanding of pre-trained models and their applications.
Data Analyst
Data Analyst
Data Analysts collect, process, and analyze data to help businesses make better decisions. By understanding how to use and fine-tune pre-trained models, you can improve the accuracy and efficiency of your analyses. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your understanding of pre-trained models and their applications.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. Pre-trained models can help you to build more accurate and sophisticated risk models. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your understanding of pre-trained models and their applications.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations and other financial decisions. Pre-trained models can help you to analyze financial data more quickly and accurately. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your understanding of pre-trained models and their applications.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks to businesses and organizations. Pre-trained models can help you to develop more accurate and robust risk assessments. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your understanding of pre-trained models and their applications.
Market Researcher
Market Researcher
Market Researchers gather and analyze data to help businesses understand their customers and markets. Pre-trained models can help you to analyze market data more quickly and accurately. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your understanding of pre-trained models and their applications.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to design and analyze medical studies. Pre-trained models can help you to design more efficient and effective studies. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your understanding of pre-trained models and their applications.
Data Engineer
Data Engineer
Data Engineers build and maintain the data infrastructure that businesses need to store and process their data. Pre-trained models can help you to build more efficient and scalable data pipelines. Applying Neural Networks: A Guide to Pre-trained Models may be helpful as it will improve your understanding of pre-trained models and their applications.
For more career information including salaries, visit:
OpenCourser.com/course/fn82l5/applying
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
Applying Neural Networks: A Guide to Pre-trained Models.
Provides a comprehensive overview of deep learning, covering the fundamentals of neural networks, convolutional neural networks, recurrent neural networks, and more. It valuable resource for anyone who wants to learn about deep learning and its applications.
Provides a practical guide to machine learning, covering the basics of supervised and unsupervised learning, feature engineering, and model evaluation. It valuable resource for anyone who wants to learn about machine learning and its applications.
Provides a comprehensive guide to TensorFlow, the leading open-source deep learning library. It valuable resource for anyone who wants to learn about TensorFlow and its applications.
Provides a comprehensive guide to Keras, the high-level deep learning library for Python. It valuable resource for anyone who wants to learn about Keras and its applications.
Provides a comprehensive guide to natural language processing with PyTorch, the leading open-source deep learning library for natural language processing. It valuable resource for anyone who wants to learn about natural language processing and its applications.
Provides a comprehensive introduction to reinforcement learning, a powerful technique for training AI agents to solve complex problems. It valuable resource for anyone who wants to learn about reinforcement learning and its applications.
Provides a comprehensive guide to machine learning with R, the leading open-source statistical programming language. It valuable resource for anyone who wants to learn about machine learning and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fn82l5/applying
Share
Similar courses
Here are nine courses similar to
Applying Neural Networks: A Guide to Pre-trained Models.
Build Solutions with Pre-trained LLMs
OpenCourser.com/course/pyzvm0/build
Build Solutions with Pre-trained LLMs
Most relevant
AI Language Models and Foundation Models
OpenCourser.com/course/587nn1/ai
AI Language Models and Foundation Models
Most relevant
Pretraining LLMs
OpenCourser.com/course/tds2vb/pretraining
Pretraining LLMs
Most relevant
Machine Learning: Modern Computer Vision & Generative AI
OpenCourser.com/course/mkbza2/machine
Machine Learning: Modern Computer Vision & Generative AI
Most relevant
Neural Networks for Data Professionals: A Comprehensive Introduction
OpenCourser.com/course/fnmmma/neural
Neural Networks for Data Professionals: A Comprehensive...
Most relevant
Applied Generative AI and Natural Language Processing
OpenCourser.com/course/0ycg41/applied
Applied Generative AI and Natural Language Processing
Most relevant
Working with Pre-trained NLP Models
OpenCourser.com/course/ouok51/working
Working with Pre-trained NLP Models
Most relevant
TensorFlow for CNNs: Transfer Learning
OpenCourser.com/course/p7kb3k/tensorflow
TensorFlow for CNNs: Transfer Learning
Most relevant
Generative AI Fundamentals
OpenCourser.com/course/8j228k/generative
Generative AI Fundamentals
Most relevant
Time
35 hours
Level
Introductory
Via
Pluralsight
Instructor
Alper Tellioglu
Language
English
Good to know
Introduces essential concepts of pre-trained models
Offers a practical understanding of pre-trained models through hands-on applications
Facilitates the integration of pre-trained models into existing projects
Provides a comprehensive overview of pre-trained models and their real-world applications
Covers advanced techniques such as fine-tuning and transfer learning for customizing pre-trained models
Suitable for learners with some background in machine learning and artificial intelligence
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser