It’s backed by industry giants like Arm, Apple, and Qualcomm Innovation Center
AMD is also trying to break NVIDIA’s CUDA monopoly in the AI parallel computing segment.
PyTorch 2.1 released a host of updates and improved their library. They also added support for training and inference of Llama 2 models powered by AWS Inferentia.
Google was leading with TensorFlow, but Meta’s PyTorch won hearts with the ease of use, and things have stayed that way
Meta currently does not care about revenue from generative AI.
The library now includes a new method in TabularModel for enabling feature importance
The push is completely towards making it more “Pythonic”.
TensorFlow reaches far beyond Python and that is what is keeping it alive—for now.
The new feature pushes PyTorch’s performance to new heights and moves some of the components of PyTorch from C++ back into Python.
The tutorial’s main goal is to help build expertise on leveraging FSDP for distributed AI training and awaits upcoming addition of new videos to the series.
The foundation would be under the watchful eye of a diverse group of board members from leading organisations like AMD, AWS, Google Cloud, and NVIDIA, among others
At Thoucentric, Manu Joseph leads the research initiatives in causality, predictive maintenance, time series forecasting, NLP and others.
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