They process "Vectorized" and "Standardized" representation. This is primary because, Neural networks do not process raw data. Python generator, that yields batch of data.ĭata preprocessing, as in the case of ETL (Extract, Transform, Load) is an important step in model training as well.Tensorflow dataset objects, which is a high-performance option that is more suitable for dataset that do not fit the in-memory and that are streamed from the disk or from distributed file system.Numpy array, which is good if our data can fit in-memory.Keras, accepts 3 types of inputs, so before we start training the model, we need to ensure that data is available in one of the below formats. The CORE data structure of Keras consists of two objects, which means Keras is primarily built of two components. Keras, is a deep learning API, a kind of a function built on top of Tensorflow, the machine learning platform. It can scale easily on multiple devices leveraging the benefits of distributed computing.It can without much complexity, execute the Tensor operations or machine learning model execution on a CPU, GPU and TPU (read Tensor processing Unit on Google Cloud Platform).As, it is a platform, it comes with few important capabilities, which also make Tensorflow little famous. It is an end to end open source Machine learning platform. Most of you may have already heard about Tensorflow.
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