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Janani Ravi
PyTorch is quickly emerging as a popular choice for building deep learning models due to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. But, as a relatively recent entrant in the fast-moving world of deep learning...
Read more
PyTorch is quickly emerging as a popular choice for building deep learning models due to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. But, as a relatively recent entrant in the fast-moving world of deep learning...
Read more
PyTorch is quickly emerging as a popular choice for building deep learning models due to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. But, as a relatively recent entrant in the fast-moving world of deep learning frameworks, PyTorch is only now being fully supported by the major cloud providers. In this course, Using PyTorch in the Cloud: PyTorch Playbook, you will gain the ability to use PyTorch on each of the big three cloud providers: Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP). First, you will learn how PyTorch can be put to use on AWS, including on AWS Sagemaker notebook instances, Amazon Machine Images (AMIs), and using the Sagemaker PyTorch estimator for distributed training. Next, you will discover how Microsoft Azure supports PyTorch, including Azure notebooks, Azure deep learning VMs, and PyTorch Estimators, which run using the Azure machine learning service. Finally, you will round out the course by understanding GCP support for PyTorch, including both Cloud Datalab (which does not have GPU support), and JupyterLab on GCP Deep Learning VMs (which does). When you are finished with this course, you will have the skills and knowledge to leverage PyTorch on each of the big three cloud providers.
Register for this course and see more details by visiting:
OpenCourser.com/course/ybyc9c/using
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Teaches how to deploy PyTorch models in cloud environments, including AWS, Azure, and GCP
Suitable for learners with a basic understanding of PyTorch and cloud computing
Instructors are experienced practitioners in the field of PyTorch and cloud computing
Requires learners to have access to cloud computing resources, which may incur costs
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Career center
Learners who complete Using PyTorch in the Cloud: PyTorch Playbook will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
The Machine Learning Engineer is responsible for designing, developing, deploying, and maintaining machine learning models. This course can help you become a Machine Learning Engineer by providing you with the skills and knowledge to use PyTorch, a popular deep learning framework, on each of the big three cloud providers: Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP).
Data Scientist
Data Scientist
The Data Scientist is responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. This course can help you become a Data Scientist by providing you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make predictions.
Deep Learning Engineer
Deep Learning Engineer
The Deep Learning Engineer is responsible for designing, developing, and deploying deep learning models. This course can help you become a Deep Learning Engineer by providing you with the skills and knowledge to use PyTorch, a popular deep learning framework, on each of the big three cloud providers: Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP).
Cloud Architect
Cloud Architect
The Cloud Architect is responsible for designing and implementing cloud-based solutions. This course can help you become a Cloud Architect by providing you with the skills and knowledge to use PyTorch on each of the big three cloud providers: Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP).
Software Engineer
Software Engineer
The Software Engineer is responsible for designing, developing, and maintaining software applications. This course can help you become a Software Engineer by providing you with the skills and knowledge to use PyTorch, a popular deep learning framework, on each of the big three cloud providers: Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP).
Risk Analyst
Risk Analyst
The Risk Analyst is responsible for identifying and assessing risks to help businesses make better decisions. This course may be useful for you if you want to become a Risk Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to identify and assess risks.
Product Manager
Product Manager
The Product Manager is responsible for developing and managing products. This course may be useful for you if you want to become a Product Manager, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make decisions about product development.
Quantitative Analyst
Quantitative Analyst
The Quantitative Analyst is responsible for using mathematical and statistical models to analyze data and make predictions. This course may be useful for you if you want to become a Quantitative Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make predictions.
Business Intelligence Analyst
Business Intelligence Analyst
The Business Intelligence Analyst is responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. This course may be useful for you if you want to become a Business Intelligence Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make predictions.
Investment Analyst
Investment Analyst
The Investment Analyst is responsible for analyzing investment data to help businesses make better decisions. This course may be useful for you if you want to become an Investment Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze investment data and make predictions.
Financial Analyst
Financial Analyst
The Financial Analyst is responsible for analyzing financial data to help businesses make better decisions. This course may be useful for you if you want to become a Financial Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze financial data and make predictions.
Marketing Manager
Marketing Manager
The Marketing Manager is responsible for developing and implementing marketing campaigns. This course may be useful for you if you want to become a Marketing Manager, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make decisions about marketing campaigns.
Operations Research Analyst
Operations Research Analyst
The Operations Research Analyst is responsible for using mathematical and statistical models to help businesses make better decisions. This course may be useful for you if you want to become an Operations Research Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to make better decisions.
Data Analyst
Data Analyst
The Data Analyst is responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. This course may be useful for you if you want to become a Data Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make predictions.
Management Consultant
Management Consultant
The Management Consultant is responsible for helping businesses improve their performance. This course may be useful for you if you want to become a Management Consultant, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make recommendations for improvement.
For more career information including salaries, visit:
OpenCourser.com/course/ybyc9c/using
Reading list
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Provides a comprehensive overview of cloud computing. It covers a wide range of topics, including cloud architectures, cloud services, and cloud security.
Primer on cloud computing. It covers the basics of cloud computing, including what it is, how it works, and its benefits.
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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 practical guide to deep learning, focusing on the design and implementation of deep learning models. 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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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.
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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.
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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.
Provides a comprehensive overview of deep learning for genomics, 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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作为一本中文著作，深入浅出地讲解了深度学习的原理、算法和应用，适合作为入门或进阶的学习教材。
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Provides a comprehensive overview of cloud computing, covering concepts, technologies, and architectures. It is written by leading experts in the field.
Great introduction to cloud computing for beginners. It covers the basics of cloud computing, including what it is, how it works, and its benefits.
Provides a theoretical foundation for cloud computing. It covers topics such as cloud architectures, cloud resource management, and cloud security.
Practical guide to cloud computing. It covers a wide range of topics, including cloud computing basics, cloud architectures, and cloud security.
Hands-on guide to cloud computing. It covers a wide range of topics, including cloud architectures, cloud services, and cloud security.
Provides a comprehensive overview of machine learning with Java, a popular programming language for enterprise applications. It covers topics such as data preprocessing, model selection, and hyperparameter tuning. It great resource for practitioners looking to get started with machine learning in Java.
For more information about how these books relate to this course, visit:
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Time
2 hours
Via
Pluralsight
Instructor
Janani Ravi
This course belongs to a
collection
called
Building Deep Learning Solutions with PyTorch. It's comprised of eight courses:
- Foundations of PyTorch
- Building Your First PyTorch Solution
- Using PyTorch in the Cloud: PyTorch Playbook (viewing)
- Image Classification with PyTorch
- Style Transfer with PyTorch
- Natural Language Processing with PyTorch
- Expediting Deep Learning with Transfer Learning: PyTorch Playbook
- Deploying PyTorch Models in Production: PyTorch Playbook
Good to know
Teaches how to deploy PyTorch models in cloud environments, including AWS, Azure, and GCP
Suitable for learners with a basic understanding of PyTorch and cloud computing
Instructors are experienced practitioners in the field of PyTorch and cloud computing
Requires learners to have access to cloud computing resources, which may incur costs
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