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Learner Reviews & Feedback for Custom and Distributed Training with TensorFlow by DeepLearning.AI

4.8
stars
304 ratings

About the Course

In this course, you will: • Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow, and learn how to use a TensorFlow tool to calculate gradients. • Build your own custom training loops using GradientTape and TensorFlow Datasets to gain more flexibility and visibility with your model training. • Learn about the benefits of generating code that runs in graph mode, take a peek at what graph code looks like, and practice generating this more efficient code automatically with TensorFlow’s tools. • Harness the power of distributed training to process more data and train larger models, faster, get an overview of various distributed training strategies, and practice working with a strategy that trains on multiple GPU cores, and another that trains on multiple TPU cores. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models....

Top reviews

VV

Jan 8, 2022

Another great course by Moroney sir. Loved how TF can be used to train models using different strategies. A great intro to the deep applications of TensorFlow

JN

Jun 22, 2022

Detail and easy to understand. I really recommend this specialization to improve training process using distributed training and strategy

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