Wentworth
Institute of Technology
TensorFlow deep dive : build, train, and deploy machine learning models with TensorFlow.
TensorFlow ( tf ) is a mainstream software platform for data science, data engineering, and MLOps. Maintained by Google and the community, this open source platform has facilitated many of the dramatic advances in AI over the past ten years. TensorFlow is used extensively across academia and industr...
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| Format: | Electronic Video |
|---|---|
| Language: | English |
| Published: |
[Place of publication not identified] :
O'Reilly Media, Inc.,
2023.
|
| Edition: | [First edition]. |
| Subjects: | |
| Online Access: |
Streaming video (Wentworth users only) |
MARC
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| 520 | |a TensorFlow ( tf ) is a mainstream software platform for data science, data engineering, and MLOps. Maintained by Google and the community, this open source platform has facilitated many of the dramatic advances in AI over the past ten years. TensorFlow is used extensively across academia and industry, and is one of the most prominent tools in a data scientist's utility belt today. In this course you'll learn about TensorFlow itself and how to use it in your daily work. What you'll learn and how you can apply it Understand the TensorFlow platform and the roles of its components Use TensorFlow to accomplish typical data science tasks: model training, optimization, inference, serving, monitoring and more Gain a working knowledge of TensorFlow APIs This course is for you because... You're a software engineer familiar with Python and would like to learn TensorFlow. You're a data scientist who knows Python and wants to diversify your tools. You want to become more proficient at machine learning with TensorFlow. TensorFlow is an adjacent technology that you would like to understand better. You want to use TensorFlow to build machine learning models. You want to get hands-on with TensorFlow and build and train a machine learning model. Prerequisites: Working knowledge of Python Data science experience a plus but not necessary Familiarity with NumPy a plus but not necessary Basics of machine learning a plus but not necessary An understanding of statistics and mathematics a plus but not necessary Familiarity with deep learning a plus but not necessary Recommended follow-up Learning TensorFlow (book) TensorFlow for Deep Learning (book) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition (book) Designing Data-Intensive Applications (book) Fundamentals of Data Engineering (book). | ||
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