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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you use convolutions to recognize features in an image where the subject can be anywhere in the image!
Register for this course and see more details by visiting:
OpenCourser.com/course/pceouh/identify
What's inside
Syllabus
Identify Horses or Humans with TensorFlow and Vertex AI
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers how to identify horses and humans with TensorFlow and Vertex AI
Self-paced lab format allows for flexibility in learning
In-console labs provide hands-on practice
Taught by Google Cloud Training, known for expertise in cloud computing
May require familiarity with TensorFlow and Vertex AI for full comprehension
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Save Identify Horses or Humans with TensorFlow and Vertex AI to your list so you can find it easily later:
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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 Identify Horses or Humans with TensorFlow and Vertex AI with these
activities:
Consolidate Lab Materials and Notes
Show steps
Improve your understanding and retention by organizing and reviewing the lab materials and your own notes.
Show steps
-
Gather all lab materials, including the syllabus, lab instructions, and any provided data.
-
Review your notes and identify any areas where you need further clarification.
-
Organize and consolidate your materials in a systematic manner.
Review Convolutional Neural Networks (CNNs)
Show steps
Strengthen your foundational knowledge of CNNs, a core concept used in this lab.
Browse courses on
Convolutional Neural Networks
Show steps
-
Read articles or watch videos on the basics of CNNs.
-
Practice implementing simple CNNs using Python or other programming languages.
Review 'Deep Learning with Python' by Francois Chollet
Show steps
Supplement your understanding of the lab concepts by reviewing an authoritative textbook on deep learning.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Purchase or access a copy of 'Deep Learning with Python'.
-
Read the chapters relevant to image recognition and convolutions.
-
Complete the exercises and practice problems provided in the book.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Attend a Workshop on Image Processing and Recognition
Show steps
Enhance your practical skills by attending a workshop focused on image processing and recognition techniques.
Show steps
-
Research and identify workshops offered by industry experts or academic institutions.
-
Register for a workshop that aligns with your learning objectives.
-
Actively participate in the workshop, ask questions, and engage with instructors.
Complete Practice Problems
Show steps
Reinforce your understanding of convolutions and image recognition by solving practice problems.
Browse courses on
TensorFlow
Show steps
-
Access the practice problems provided by Google Cloud Training.
-
Attempt to solve the problems using the concepts discussed in the lab.
-
Review your solutions against the provided answer key.
-
Identify areas where you need further clarification and seek support from the course instructors or online forums.
Join a Study Group for Image Recognition
Show steps
Enhance your learning through collaboration and discussion with peers.
Show steps
-
Identify or form a study group with other students enrolled in the course.
-
Establish regular meetings to discuss lab concepts, share insights, and work on projects together.
Explore Advanced TensorFlow Techniques
Show steps
Expand your knowledge of TensorFlow by exploring advanced techniques that can enhance your image recognition capabilities.
Show steps
-
Identify advanced TensorFlow techniques, such as transfer learning or data augmentation.
-
Follow online tutorials or documentation to implement these techniques in your own projects.
-
Experiment with different parameters and settings to optimize your results.
Build Your Own Image Recognition Model
Show steps
Deepen your understanding of the course concepts by creating your own image recognition model using the techniques learned in the lab.
Show steps
-
Gather a dataset of images for your desired classification task.
-
Preprocess the images using techniques covered in the lab, such as resizing and normalization.
-
Design and train a convolutional neural network (CNN) using TensorFlow or Vertex AI.
-
Evaluate the performance of your model on a validation set.
-
Deploy your model and test its accuracy on new images.
Create a Presentation on Image Recognition Techniques
Show steps
Demonstrate your mastery of image recognition techniques by creating a presentation for a technical audience.
Show steps
-
Research and gather information on various image recognition techniques beyond the scope of the lab.
-
Organize your presentation to provide a clear and comprehensive overview of the techniques.
-
Use visual aids, such as diagrams and examples, to enhance your presentation.
-
Practice delivering your presentation to ensure clarity and engagement.
Consolidate Lab Materials and Notes
Show steps
Improve your understanding and retention by organizing and reviewing the lab materials and your own notes.
Show steps
Show steps
Show steps
- Gather all lab materials, including the syllabus, lab instructions, and any provided data.
- Review your notes and identify any areas where you need further clarification.
- Organize and consolidate your materials in a systematic manner.
Review Convolutional Neural Networks (CNNs)
Show steps
Strengthen your foundational knowledge of CNNs, a core concept used in this lab.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Read articles or watch videos on the basics of CNNs.
- Practice implementing simple CNNs using Python or other programming languages.
Review 'Deep Learning with Python' by Francois Chollet
Show steps
Supplement your understanding of the lab concepts by reviewing an authoritative textbook on deep learning.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Purchase or access a copy of 'Deep Learning with Python'.
- Read the chapters relevant to image recognition and convolutions.
- Complete the exercises and practice problems provided in the book.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Attend a Workshop on Image Processing and Recognition
Show steps
Enhance your practical skills by attending a workshop focused on image processing and recognition techniques.
Show steps
Show steps
Show steps
- Research and identify workshops offered by industry experts or academic institutions.
- Register for a workshop that aligns with your learning objectives.
- Actively participate in the workshop, ask questions, and engage with instructors.
Complete Practice Problems
Show steps
Reinforce your understanding of convolutions and image recognition by solving practice problems.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Access the practice problems provided by Google Cloud Training.
- Attempt to solve the problems using the concepts discussed in the lab.
- Review your solutions against the provided answer key.
- Identify areas where you need further clarification and seek support from the course instructors or online forums.
Join a Study Group for Image Recognition
Show steps
Enhance your learning through collaboration and discussion with peers.
Show steps
Show steps
Show steps
- Identify or form a study group with other students enrolled in the course.
- Establish regular meetings to discuss lab concepts, share insights, and work on projects together.
Explore Advanced TensorFlow Techniques
Show steps
Expand your knowledge of TensorFlow by exploring advanced techniques that can enhance your image recognition capabilities.
Show steps
Show steps
Show steps
- Identify advanced TensorFlow techniques, such as transfer learning or data augmentation.
- Follow online tutorials or documentation to implement these techniques in your own projects.
- Experiment with different parameters and settings to optimize your results.
Build Your Own Image Recognition Model
Show steps
Deepen your understanding of the course concepts by creating your own image recognition model using the techniques learned in the lab.
Show steps
Show steps
Show steps
- Gather a dataset of images for your desired classification task.
- Preprocess the images using techniques covered in the lab, such as resizing and normalization.
- Design and train a convolutional neural network (CNN) using TensorFlow or Vertex AI.
- Evaluate the performance of your model on a validation set.
- Deploy your model and test its accuracy on new images.
Create a Presentation on Image Recognition Techniques
Show steps
Demonstrate your mastery of image recognition techniques by creating a presentation for a technical audience.
Show steps
Show steps
Show steps
- Research and gather information on various image recognition techniques beyond the scope of the lab.
- Organize your presentation to provide a clear and comprehensive overview of the techniques.
- Use visual aids, such as diagrams and examples, to enhance your presentation.
- Practice delivering your presentation to ensure clarity and engagement.
Career center
Learners who complete Identify Horses or Humans with TensorFlow and Vertex AI will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop, test, and maintain computer vision systems. They use their knowledge of image processing, machine learning, and computer programming to create systems that can identify and classify objects in images. This course provides a foundation in computer vision concepts and techniques, which can be applied to a variety of applications, including object recognition, facial recognition, and medical imaging.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, machine learning, and computer programming to extract insights from data. They work on a variety of projects, including developing predictive models, identifying trends, and optimizing business processes. This course provides a foundation in machine learning and image processing, which are essential skills for Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They use their knowledge of machine learning algorithms, cloud computing, and software engineering to create systems that can learn from data and make predictions. This course provides a foundation in machine learning and image processing, which are essential skills for Machine Learning Engineers.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their knowledge of programming languages, software engineering principles, and computer science to create software that meets the needs of users. This course provides a foundation in computer vision and machine learning, which are becoming increasingly important in software development.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their knowledge of statistics, data mining, and data visualization to communicate their findings to stakeholders. This course provides a foundation in image processing and machine learning, which can be used to analyze images and extract insights from them.
Business Analyst
Business Analyst
Business Analysts identify and analyze business needs and develop solutions to improve business processes. They use their knowledge of business processes, data analysis, and technology to create solutions that meet the needs of stakeholders. This course provides a foundation in image processing and machine learning, which can be used to analyze data and identify trends.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They use their knowledge of project management principles, risk management, and stakeholder management to lead projects to successful completion. This course provides a foundation in image processing and machine learning, which can be used to analyze data and identify trends.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test and evaluate software systems to ensure that they meet quality standards. They use their knowledge of software testing principles, quality assurance techniques, and programming languages to identify and fix defects. This course provides a foundation in image processing and machine learning, which can be used to automate testing and improve quality.
IT Consultant
IT Consultant
IT Consultants provide advice and support to businesses on how to use technology to improve their operations. They use their knowledge of technology, business processes, and project management to help businesses achieve their goals. This course provides a foundation in image processing and machine learning, which can be used to analyze data and identify trends.
UX Designer
UX Designer
UX Designers design user interfaces for websites, apps, and other software products. They use their knowledge of human factors, design principles, and user research to create interfaces that are easy to use and enjoyable to use. This course provides a foundation in image processing and machine learning, which can be used to analyze user behavior and improve the user experience.
Videographer
Videographer
Videographers shoot, edit, and produce videos. They use their knowledge of videography techniques, lighting, and composition to create visually appealing videos. This course provides a foundation in image processing and machine learning, which can be used to edit and enhance videos.
Game Developer
Game Developer
Game Developers design, develop, and test video games. They use their knowledge of programming languages, game design principles, and computer graphics to create games that are fun and engaging to play. This course provides a foundation in image processing and machine learning, which can be used to create more realistic and immersive games.
Graphic designer
Graphic designer
Graphic Designers create visual content for a variety of purposes, including websites, print materials, and social media. They use their knowledge of design principles, typography, and color theory to create visually appealing designs. This course provides a foundation in image processing and machine learning, which can be used to automate tasks and improve the quality of designs.
Photographer
Photographer
Photographers take pictures of people, places, and things. They use their knowledge of photography techniques, lighting, and composition to create visually appealing images. This course provides a foundation in image processing and machine learning, which can be used to edit and enhance images.
Animator
Animator
Animators create moving images using a variety of techniques, including 2D animation, 3D animation, and stop-motion animation. They use their knowledge of animation principles, character design, and storytelling to create visually appealing animations. This course provides a foundation in image processing and machine learning, which can be used to automate tasks and improve the quality of animations.
For more career information including salaries, visit:
OpenCourser.com/course/pceouh/identify
Reading list
We've selected 13 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
Identify Horses or Humans with TensorFlow and Vertex AI.
Provides a comprehensive introduction to deep learning, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone looking to gain a deeper understanding of deep learning and its applications.
Provides a practical guide to using Scikit-Learn, Keras, and TensorFlow for machine learning. It covers the basics of machine learning, as well as more advanced topics such as natural language processing and computer vision.
Provides a comprehensive guide to using deep learning for computer vision. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks and object detection.
Provides a comprehensive overview of statistical learning, covering the fundamental concepts and algorithms used in the field. It valuable resource for anyone looking to gain a deeper understanding of statistical learning and its applications.
Provides a comprehensive overview of pattern recognition and machine learning, covering the fundamental concepts and algorithms used in the field. It valuable resource for anyone looking to gain a deeper understanding of pattern recognition and machine learning and its applications.
Save
Provides a comprehensive overview of machine learning, covering the fundamental concepts and algorithms used in the field. It valuable resource for anyone looking to gain a deeper understanding of machine learning and its applications.
Save
Provides a comprehensive overview of deep learning, covering the fundamental concepts and algorithms used in the field. It valuable resource for anyone looking to gain a deeper understanding of deep learning and its applications.
Save
Provides a comprehensive overview of computer vision, covering the fundamental concepts and algorithms used in the field. It valuable resource for anyone looking to gain a deeper understanding of computer vision and its applications.
Provides a comprehensive overview of probabilistic graphical models, covering the fundamental concepts and algorithms used in the field. It valuable resource for anyone looking to gain a deeper understanding of probabilistic graphical models and its applications.
Provides a comprehensive overview of reinforcement learning, covering the fundamental concepts and algorithms used in the field. It valuable resource for anyone looking to gain a deeper understanding of reinforcement learning and its applications.
Provides a comprehensive overview of natural language processing, covering the fundamental concepts and algorithms used in the field. It valuable resource for anyone looking to gain a deeper understanding of natural language processing and its applications.
Provides a comprehensive overview of information theory, inference, and learning algorithms, covering the fundamental concepts and algorithms used in the field. It valuable resource for anyone looking to gain a deeper understanding of information theory, inference, and learning algorithms and its applications.
Save
Provides a comprehensive overview of data mining, covering the fundamental concepts and algorithms used in the field. It valuable resource for anyone looking to gain a deeper understanding of data mining and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pceouh/identify
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Effort
90 minutes
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Covers how to identify horses and humans with TensorFlow and Vertex AI
Self-paced lab format allows for flexibility in learning
In-console labs provide hands-on practice
Taught by Google Cloud Training, known for expertise in cloud computing
May require familiarity with TensorFlow and Vertex AI for full comprehension
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