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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Read more
This is a self-paced lab that takes place in the Google Cloud console.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Read more
This is a self-paced lab that takes place in the Google Cloud console.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
This lab uses transfer learning to train your machine. In transfer learning, when you build a new model to classify your original dataset, you reuse the feature extraction part and re-train the classification part with your dataset. This method uses less computational resources and training time. Deep learning from scratch can take days, but transfer learning can be done in short order.
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches transfer learning, which is a highly relevant technique in deep learning that is used across disciplines and industries
Relevant for beginners as it uses transfer learning, which requires fewer computational resources and training time than deep learning from scratch
Provides hands-on practice with transfer learning in the Google Cloud console
Offered by Google Cloud Training, which is recognized for its expertise in cloud computing and machine learning
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Reviews summary
Practical transfer learning with google cloud
According to learners, this course is a highly practical and hands-on lab focusing on image classification using transfer learning on Google Cloud. Students appreciate its ability to provide a clear introduction to TensorFlow on GCP, making complex concepts accessible for those looking to build practical skills. While many find the instructions clear and easy to follow, some experienced minor UI discrepancies within the Google Cloud console. It's considered excellent for beginners and intermediate learners seeking a quick, effective way to apply machine learning, though more advanced practitioners might find it too simplistic for a deep theoretical dive. The self-paced format is widely praised for its flexibility, and the course is seen as valuable for boosting one's portfolio in ML.
Instructions are generally clear, with minor UI inconsistencies.
"The instructions were clear and I successfully implemented the image classification model."
"The steps were well-documented, though sometimes I wished for a bit more explanation on why certain parameters were chosen."
"The instructions were mostly easy to follow, but a couple of times I encountered minor discrepancies with the GCP console UI."
Offers direct application of TensorFlow within Google Cloud.
"The Google Cloud environment was straightforward to navigate, and the practical application of TensorFlow was very valuable."
"Running it in Google Cloud was seamless once I got my environment set up."
"I successfully implemented the image classification model, a great way to get started with TensorFlow on GCP."
A solid introduction to the core concepts of transfer learning.
"A solid lab for anyone looking to understand transfer learning with a real-world example."
"This lab made the transfer learning concept concrete and practical."
"I found the concept of reusing pre-trained models fascinating and efficient."
Provides valuable practical experience for real-world ML tasks.
"This lab was incredibly practical and provided a hands-on introduction to transfer learning on Google Cloud."
"Excellent and concise lab. It perfectly showcased the power of transfer learning for image classification."
"This is exactly what I needed to boost my portfolio and get comfortable with ML on GCP."
Ideal for beginners, but possibly simplistic for advanced users.
"For someone already familiar with basic TensorFlow or Keras, it felt a bit too simplistic. I was hoping for more advanced techniques."
"Good for beginners to intermediate users who want practical GCP experience."
"This course assumed a bit too much prior knowledge of Google Cloud for me. I spent more time trying to navigate the GCP interface."
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 Classify Images of Cats and Dogs using Transfer Learning with these
activities:
Attend a Workshop on Transfer Learning Applications
Show steps
Gain hands-on experience and learn about real-world applications of transfer learning.
Browse courses on
Deep Learning Applications
Show steps
-
Research and find a relevant workshop.
-
Register and attend the workshop.
-
Actively participate in the sessions and discussions.
Build a Transfer Learning Model for a Custom Dataset
Show steps
Solidify your understanding by applying transfer learning to a unique dataset of your choice.
Browse courses on
Machine Learning Projects
Show steps
-
Define the problem and gather a custom dataset.
-
Select a suitable pre-trained model.
-
Build, train, and evaluate the transfer learning model.
-
Write a report documenting your approach and results.
Participate in a Transfer Learning Hackathon
Show steps
Challenge yourself and apply your transfer learning skills in a competitive setting.
Show steps
-
Find and register for a relevant hackathon.
-
Form a team or work individually.
-
Develop and submit a transfer learning solution.
Show all three activities
Attend a Workshop on Transfer Learning Applications
Show steps
Gain hands-on experience and learn about real-world applications of transfer learning.
Browse courses on
Deep Learning Applications
Show steps
Show steps
Show steps
- Research and find a relevant workshop.
- Register and attend the workshop.
- Actively participate in the sessions and discussions.
Build a Transfer Learning Model for a Custom Dataset
Show steps
Solidify your understanding by applying transfer learning to a unique dataset of your choice.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Define the problem and gather a custom dataset.
- Select a suitable pre-trained model.
- Build, train, and evaluate the transfer learning model.
- Write a report documenting your approach and results.
Participate in a Transfer Learning Hackathon
Show steps
Challenge yourself and apply your transfer learning skills in a competitive setting.
Show steps
Show steps
Show steps
- Find and register for a relevant hackathon.
- Form a team or work individually.
- Develop and submit a transfer learning solution.
Show all three activities
Career center
Learners who complete Classify Images of Cats and Dogs using Transfer Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists analyze data to extract information that can be used to make better business decisions. This course can help you build a foundation in machine learning, which is a key skill for Data Scientists. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models. This course can help you build a foundation in machine learning, which is a key skill for Machine Learning Engineers. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you build a foundation in machine learning, which is becoming increasingly important in software development. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. This course can help you build a foundation in machine learning, which is becoming increasingly important in data analysis. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Business Analyst
Business Analyst
Business Analysts help businesses understand their data and make better decisions. This course can help you build a foundation in machine learning, which is becoming increasingly important in business analysis. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course can help you build a foundation in machine learning, which is becoming increasingly important in product development. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Marketing Analyst
Marketing Analyst
Marketing Analysts help businesses understand their customers and develop marketing campaigns. This course can help you build a foundation in machine learning, which is becoming increasingly important in marketing analysis. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Financial Analyst
Financial Analyst
Financial Analysts help businesses make investment decisions. This course can help you build a foundation in machine learning, which is becoming increasingly important in financial analysis. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts help businesses solve complex problems. This course can help you build a foundation in machine learning, which is becoming increasingly important in operations research. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can help you build a foundation in machine learning, which is becoming increasingly important in quantitative analysis. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. This course can help you build a foundation in machine learning, which is becoming increasingly important in actuarial science. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course can help you build a foundation in machine learning, which is becoming increasingly important in statistics. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines. This course can help you build a foundation in machine learning, which is becoming increasingly important in data engineering. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
Cloud Architect
Cloud Architect
Cloud Architects design and implement cloud computing solutions. This course can help you build a foundation in machine learning, which is becoming increasingly important in cloud computing. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
DevOps Engineer
DevOps Engineer
DevOps Engineers build and maintain software applications. This course can help you build a foundation in machine learning, which is becoming increasingly important in DevOps. The course will teach you how to use TensorFlow, a popular open-source machine learning library, to build and train machine learning models. You will also learn how to use transfer learning to train your models more efficiently.
For more career information including salaries, visit:
OpenCourser.com/course/ysv04l/classify
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
Classify Images of Cats and Dogs using Transfer Learning.
Focuses on using TensorFlow for deep learning, covering topics such as building and training neural networks, working with large datasets, and deploying models. It provides a practical guide to applying TensorFlow in real-world scenarios.
Provides a thorough introduction to deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It offers a practical approach to deep learning using Python libraries like Keras and TensorFlow.
Save
Provides a comprehensive overview of convolutional neural networks (CNNs), covering topics such as CNN architectures, training techniques, and applications in computer vision. It valuable resource for understanding the theory and practice of CNNs.
Covers the basics of machine learning, including supervised and unsupervised learning, and uses Python libraries like Scikit-Learn, Keras, and TensorFlow. It provides a comprehensive overview of machine learning concepts and techniques.
Covers the basics of recurrent neural networks (RNNs), including different RNN architectures, training techniques, and applications in natural language processing and speech recognition. It provides a comprehensive overview of RNNs and their practical use.
Provides an introduction to generative adversarial networks (GANs), covering topics such as GAN architectures, training techniques, and applications in image generation and text generation. It offers a thorough understanding of GANs and their potential.
Covers the basics of diffusion models, including different diffusion models architectures, training techniques, and applications in image generation and text generation. It provides a comprehensive overview of diffusion models and their potential.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ysv04l/classify
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