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Janani Ravi
Transfer learning refers to the re-use of a trained machine learning model for a similar problem, keeping the model architecture unchanged, but potentially altering the model’s weights. In this course, Expediting Deep Learning with Transfer Learning:...
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Transfer learning refers to the re-use of a trained machine learning model for a similar problem, keeping the model architecture unchanged, but potentially altering the model’s weights. In this course, Expediting Deep Learning with Transfer Learning: PyTorch Playbook, you will gain the ability to identify the right approach to transfer learning, and implement it using PyTorch. First, you will learn how different forms of transfer learning - such as inductive, transductive, and unsupervised transfer learning - can be applied to different combinations of source and target domains. Next, you will discover how transfer learning solutions leverage the fact that lower layers of pre-trained models typically extract feature information and are data-specific, while later layers tend to be more problem-specific. Finally, you will explore how to design and implement the correct strategy for freezing and fine-tuning the appropriate layers of your pre-trained model. You will round out the course by seeing how various powerful architectures are made available, in pre-trained form, in PyTorch’s suite of transfer learning solutions. When you’re finished with this course, you will have the skills and knowledge to choose the right transfer learning approach to your specific problem, and design and implement it using PyTorch.
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Teaches transfer learning, which is highly relevant for accelerating deep learning tasks
Explores various transfer learning approaches, including inductive, transductive, and unsupervised, making it versatile for different scenarios
Provides practical guidance on implementing transfer learning in PyTorch, a popular deep learning framework
Equips learners with strategies for freezing and fine-tuning pre-trained models, enabling customization for specific tasks
Facilitates understanding of how pre-trained models extract feature information, making it useful for feature engineering

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Career center

Learners who complete Expediting Deep Learning with Transfer Learning: PyTorch Playbook will develop knowledge and skills that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineers design, develop, and deploy deep learning models. Transfer learning is a key technique used by Deep Learning Engineers to improve the performance of deep learning models. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Deep Learning Engineer.
Data Scientist
Data Scientists use machine learning and other advanced analytical techniques to extract insights from data. Transfer learning is a key technique used by Data Scientists to accelerate the development of machine learning models. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. Transfer learning is a key technique used by Machine Learning Engineers to improve the performance of machine learning models. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Machine Learning Engineer.
Business Analyst
Business Analysts use data to solve business problems. Transfer learning is a key technique used by Business Analysts to improve the performance of business analysis models. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Business Analyst.
Software Engineer
Software Engineers design, develop, and maintain software applications. Transfer learning is a key technique used by Software Engineers to improve the performance of software applications. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Software Engineer.
Data Analyst
Data Analysts use data to solve business problems. Transfer learning is a key technique used by Data Analysts to improve the performance of data analysis models. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Data Analyst.
Project Manager
Project Managers plan and execute projects. Transfer learning is a key technique used by Project Managers to improve the performance of projects. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Project Manager.
Product Manager
Product Managers develop and manage software products. Transfer learning is a key technique used by Product Managers to improve the performance of software products. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Product Manager.
Technical Writer
Technical Writers create documentation for software products. Transfer learning is a key technique used by Technical Writers to improve the performance of documentation. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Technical Writer.
Security Analyst
Security Analysts design and manage security systems. Transfer learning is a key technique used by Security Analysts to improve the performance of security systems. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Security Analyst.
Data Engineer
Data Engineers design and build data pipelines. Transfer learning is a key technique used by Data Engineers to improve the performance of data pipelines. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Data Engineer.
Database Administrator
Database Administrators design and manage databases. Transfer learning is a key technique used by Database Administrators to improve the performance of databases. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Database Administrator.
Systems Administrator
Systems Administrators design and manage computer systems. Transfer learning is a key technique used by Systems Administrators to improve the performance of computer systems. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Systems Administrator.
Network Administrator
Network Administrators design and manage computer networks. Transfer learning is a key technique used by Network Administrators to improve the performance of computer networks. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Network Administrator.
Risk Analyst
Risk Analysts design and manage risk management systems. Transfer learning is a key technique used by Risk Analysts to improve the performance of risk management systems. This course will provide you with the skills and knowledge to implement transfer learning using PyTorch, which is one of the most popular frameworks for deep learning. As a result, this course may be useful for anyone looking to enter or advance their career as a Risk Analyst.

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Provides a comprehensive overview of transfer learning for computer vision, covering both theoretical foundations and practical applications. It is written by leading researchers in the field and includes a wealth of examples and case studies.
Provides a comprehensive overview of transfer learning for speech and audio processing, covering both theoretical foundations and practical applications. It is written by leading researchers in the field and includes a wealth of examples and case studies.
Provides a comprehensive overview of transfer learning for robotics, covering both theoretical foundations and practical applications. It is written by leading researchers in the field and includes a wealth of examples and case studies.
Provides a comprehensive overview of transfer learning for finance, covering both theoretical foundations and practical applications. It is written by a leading researcher in the field and includes a wealth of examples and case studies.
Provides a comprehensive overview of transfer learning for marketing, covering both theoretical foundations and practical applications. It is written by a leading researcher in the field and includes a wealth of examples and case studies.
Provides a comprehensive overview of transfer learning for the arts, covering both theoretical foundations and practical applications. It is written by a leading researcher in the field and includes a wealth of examples and case studies.
Provides a comprehensive overview of transfer learning for the sciences, covering both theoretical foundations and practical applications. It is written by a leading researcher in the field and includes a wealth of examples and case studies.
Provides a comprehensive overview of transfer learning for engineering, covering both theoretical foundations and practical applications. It is written by a leading researcher in the field and includes a wealth of examples and case studies.
Provides a comprehensive overview of PyTorch, covering all the key concepts and techniques needed to build and train deep learning models effectively. It also includes practical examples and exercises.
Provides a hands-on introduction to PyTorch, focusing on practical examples and applications. It good starting point for beginners who want to learn how to use PyTorch.
Provides a comprehensive overview of deep learning, covering the fundamental concepts, algorithms, and applications. It is written by three leading researchers in the field and is considered one of the most authoritative resources on deep learning.
Provides a hands-on introduction to deep learning using the Python programming language. It is written by the creator of the Keras deep learning library and is known for its practical examples and clear explanations.
Provides a comprehensive overview of deep learning for natural language processing, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is considered one of the most authoritative resources on deep learning for NLP.
Provides a practical guide to deep learning for computer vision, focusing on the design and implementation of deep learning models for image and video processing. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
Provides a comprehensive overview of deep learning for finance, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
Provides a comprehensive overview of deep learning for robotics, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
Provides a comprehensive overview of deep learning for materials science, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
Provides a comprehensive overview of deep learning for climate science, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
Provides a comprehensive overview of deep learning for transportation, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.

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