Coursera MOOC / Non-credit 0

Custom and Distributed Training with TensorFlow

Name: Custom and Distributed Training with TensorFlow
Price: 49 USD
Availability: InStock

About this 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.

75/100

CourseAsk score

What the provider tells you: 39/45
Who stands behind it: 20/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

Understand Tensor objects and their role in TensorFlow
Differentiate between eager and graph modes
Build custom training loops using GradientTape
Use TensorFlow Datasets for model training
Implement distributed training strategies for GPUs and TPUs

Course objectives

To provide a deeper understanding of TensorFlow's advanced features
To develop practical skills in custom training loops
To enhance knowledge of distributed training techniques

Deep Learning #deep learning #machine learning #model training #tensorflow #distributed training #GPU training #gradient tape #custom training loops #tpu training #tensorflow datasets

$49.00

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