Image Understanding with TensorFlow on GCP
This course provides an overview of strategies for building an image classifier using convolutional neural networks on Google Cloud Platform. Participants will learn how to improve accuracy with augmentation, feature extraction, and hyperparameter tuning, as well as how to address practical issues such as data scarcity. Through hands-on labs, participants will gain experience building and optimizing image classification models on public datasets. ▼
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Course Feature
Cost:
Free Trial
Provider:
Pluralsight
Certificate:
Paid Certification
Language:
English
Start Date:
On-Demand
Course Overview
❗The content presented here is sourced directly from Pluralsight platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
This course provides an overview of image understanding with TensorFlow on GCP. Participants will learn how to build an image classifier using convolutional neural networks, improve the model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters, and address practical issues such as when there is not enough data. Participants will also get hands-on practice building and optimizing their own image classification models on a variety of public datasets.
[Applications]
Upon completion of this course, participants should be able to apply the knowledge and skills acquired to build and optimize their own image classification models on Google Cloud Platform (GCP). They should also be able to identify and address practical issues that arise when working with image data, such as data augmentation, feature extraction, and hyperparameter tuning. Additionally, participants should be able to incorporate the latest research findings into their models.
[Career Paths]
1. Machine Learning Engineer: Machine learning engineers are responsible for developing and deploying machine learning models. They must have a strong understanding of the fundamentals of machine learning, as well as the ability to use frameworks such as TensorFlow and GCP to build and deploy models. As the demand for machine learning increases, so does the need for machine learning engineers.
2. Data Scientist: Data scientists are responsible for analyzing data and extracting insights from it. They must have a strong understanding of statistics and data analysis techniques, as well as the ability to use frameworks such as TensorFlow and GCP to build and deploy models. As the demand for data-driven insights increases, so does the need for data scientists.
3. Computer Vision Engineer: Computer vision engineers are responsible for developing and deploying computer vision models. They must have a strong understanding of the fundamentals of computer vision, as well as the ability to use frameworks such as TensorFlow and GCP to build and deploy models. As the demand for computer vision applications increases, so does the need for computer vision engineers.
4. Artificial Intelligence Engineer: Artificial intelligence engineers are responsible for developing and deploying AI models. They must have a strong understanding of the fundamentals of AI, as well as the ability to use frameworks such as TensorFlow and GCP to build and deploy models. As the demand for AI applications increases, so does the need for AI engineers.
[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and deep learning. This degree path is becoming increasingly popular as the demand for computer scientists with knowledge of machine learning and deep learning grows.
2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence systems, including natural language processing, computer vision, and robotics. It also covers topics such as machine learning, deep learning, and reinforcement learning. This degree path is becoming increasingly popular as the demand for AI experts with knowledge of machine learning and deep learning grows.
3. Master of Science in Data Science: This degree path focuses on the fundamentals of data science, including data analysis, data mining, and machine learning. It also covers topics such as artificial intelligence, deep learning, and natural language processing. This degree path is becoming increasingly popular as the demand for data scientists with knowledge of machine learning and deep learning grows.
4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of machine learning algorithms and systems, including deep learning, reinforcement learning, and natural language processing. It also covers topics such as artificial intelligence, computer vision, and robotics. This degree path is becoming increasingly popular as the demand for machine learning experts with knowledge of deep learning and artificial intelligence grows.
Course Provider
Provider Pluralsight's Stats at AZClass
Pluralsight ranked 16th on the Best Medium Workplaces List.
Pluralsight ranked 20th on the Forbes Cloud 100 list of the top 100 private cloud companies in the world.
Pluralsight Ranked on the Best Workplaces for Women List for the second consecutive year.
AZ Class hope that this free trial Pluralsight course can help your Tensorflow skills no matter in career or in further education. Even if you are only slightly interested, you can take Image Understanding with TensorFlow on GCP course with confidence!
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Quiz
Submitted Sucessfully
1. What is the main focus of this course?
2. What will you get hands-on practice with in this course?
3. What strategies will be discussed in this course?
4. What is the main goal of this course?
Correct Answer: To build and optimize an image classification model.
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