Dataset Preparation: Part Two, the Data Loader
In this chapter, you’ll learn how to prepare your dataset to immediately use it with your chosen models. You’ll master the concept of a data loader, learning why it’s a common source of errors in training large models. You’ll learn about creating embeddings, using tokenizers, and other methods to featurize your raw data for your preferred neural network. Following these steps, you’ll be able to prepare your entire dataset, using methods for both vision and language. Finally, you’ll learn about data optimization on AWS and Amazon SageMaker to efficiently send datasets large and small to your training cluster. Throughout this chapter, we’ll work backward through the training loop, incrementally giving you all the steps you need to have functional deep neural networks training at scale. You’ll also follow a case study on how I trained...
