Trax, an end-to-end library for deep learning developed by Google. It is designed to be easy to use, with clear with good speed, with the ability to run on modern hardware such as GPUs and TPUs.The Google Brain team actively uses and maintains Trax. It is built on top of the JAX and TensorFlow numpy, which provides automatic differentiation, a set of numerical operations, and support for GPU acceleration. It includes a wide range of pre-built models and algorithms. In addition to its extensive selection of models and algorithms, it also has bindings to a large number of deep learning datasets, including Tensor2Tensor and TensorFlow datasets. The following code creates a Transformer model for machine translation, initialises it with pre-trained weights, tokenizes an input sentence, decodes the model's output, and then detokenizes the output to get the translation.

Every once in a while, a python library is developed that has the potential of changing the landscape in the field of deep learning. PyTorch is one such library. In the last few weeks, I have been dabbling a bit in PyTorch. I have been blown away by how easy it is to grasp. Among the various deep learning libraries I have used till date – PyTorch has been the most flexible and effortless of them all.

Introduction Every once in a while, a python library is developed that has the potential of changing the landscape in the field of deep learning. PyTorch is one such library. In the last few weeks, I have been dabbling a bit in PyTorch.