Over the last several years, machine learning (ML) has transitioned from a discipline once reserved for researchers and PhDs into a lucrative field comprised of a growing and diversifying set of users and ML practitioners. Introducing PerceptiLabs - A GUI and Visual API for TensorFlow
Over the last several years, machine learning (ML) has transitioned from a discipline once reserved for researchers and PhDs into a lucrative field comprised of a growing and diversifying set of users and ML practitioners.
This is due in part to the increased processing power found in today’s hardware, the discovery of new ML algorithms, and the growing number of open source ML tools, frameworks, and datasets. Collectively, these factors are democratizing ML by putting new and more powerful ML capabilities into the hands of more users and ML practitioners than ever before.
However, despite all of these advances, many ML tools and frameworks fail to address the overall workflow of designing, training, and tuning ML models. Many frameworks such as TensorFlow and PyTorch, while incredibly powerful, are still fundamentally programmatic frameworks aimed at coders.
This not only makes it difficult to visualize, debug, and iterate on ML models, but can create a barrier to entry for those learning ML or even a specific framework (e.g., TensorFlow) for the first time.
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