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Name: Snehal Patel

Type: User

Bio: Currently 84.51º, previously eContext.ai

Location: Chicago

Senior Machine Learning Engineer/Data Scientist, AWS certified ML specialist, TensorFlow certified developer with background in natural language processing, computer vision and speech recognition.
Currently focused on developing valuable scientific solutions for search products for the 84.51º Digital Relevancy Science team.
Previously, worked towards research and development of deep learning applications for structured/unstructured data to create a scalable system for eContext's taxonomy of 500,000 categories.
Good grasp of data analysis, data cleaning, deep learning algorithms and training train low bias, low variance models.
Theoretical knowledge of the underpinnings of algorithms and statistics.
Strong expertise to grok research papers from different domains of deep learning.
Adroitness to understand business problems and building data-driven solutions.
High proficiency with deploying large scale models in production with TensorFlow/TF-Serving, PyTorch/TorchServe.

Licenses & Certifications:

GitHub Stats:

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Snehal Patel's Projects

abhisheknaiidu

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array-mixer

Powerful, super tiny and easy to use lib to reorder your javascript arrays.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

awesome-public-datasets

An awesome list of high-quality open datasets in public domains (on-going).

big-bench

Beyond the Imitation Game collaborative benchmark for measuring and extrapolating the capabilities of language models

big-list-of-naughty-strings

The Big List of Naughty Strings is a list of strings which have a high probability of causing issues when used as user-input data.

cdec

Decoder, aligner, and model optimizer for statistical machine translation and other structured prediction models based on (mostly) context-free formalisms