DeepLearning.AI's "Custom and Distributed Training with TensorFlow" course offers advanced techniques for harnessing the power of TensorFlow. Throughout this comprehensive course, learners will delve into Tensor objects, custom training loops, graph mode, and distributed training to enhance their model-building and training skills.
The course begins with a focus on Tensor objects and the differentiation and gradients needed for machine learning. Learners will then progress to building custom training loops using GradientTape and TensorFlow Datasets, gaining flexibility and visibility with model training.
Subsequently, the course delves into the benefits of generating code that runs in graph mode, offering a glimpse of what graph code looks like and providing practice in generating this more efficient code automatically with TensorFlow’s tools. Learners will also harness the power of distributed training to process more data and train larger models faster, gaining an overview of various distributed training strategies and practicing working with strategies for multiple GPU and TPU cores.
By the end of the course, learners will have expanded their knowledge and skill set, mastering advanced TensorFlow features to build powerful machine learning models.
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This course covers advanced topics such as differentiation and gradients, custom training, graph mode, and distributed training. Learners will gain in-depth knowledge and practical skills for advanced TensorFlow techniques.
This module provides an in-depth understanding of differentiation and gradients, essential for machine learning. Learners will master Tensor objects, create custom training loops using GradientTape and TensorFlow Datasets, and explore the benefits of generating code in graph mode.
Learners will explore the intricacies of custom training, including defining training loops, validating models, and understanding training steps and data pipelines. Practical examples, such as Breast Cancer Prediction and Fashion MNIST, will enhance the understanding of custom training loops.
This module focuses on the benefits of graph mode and provides practical guidance on generating graph code. Learners will gain insight into AutoGraph basics and apply this knowledge to real-world examples such as "Horse or Human?"
Learners will be introduced to the concepts of distributed training, including various distribution strategies such as Mirrored Strategy and TPU Strategy. Practical walkthroughs and examples will reinforce the understanding of distributed training strategies.
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