14–17 Oct 2019
Akshay Kulkarni

Akshay Kulkarni
Senior Data Scientist, Publicis Sapient

Akshay Kulkarni is a senior data scientist with SapientRazorfish’s core AI and data science team, where he’s part of strategy and transformation interventions through AI, manages high priority growth initiatives around data science and works on various machine learning, deep learning, natural language processing, and artificial intelligence engagements by applying state-of-the-art techniques, as well as a renowned AI and machine learning evangelist, an author, and a speaker. He was recently recognized as one of the “top 40 under 40 data scientists” in India by Analytics India Magazine. He’s consulted with several Fortune 500 and global enterprises in driving AI and data science-led strategic transformations. Akshay has a rich experience of building and scaling AI and machine learning businesses and creating significant client impact. He’s actively involved in next gen AI research and is also a part of next gen AI community. Previously, he was part of Gartner and Accenture, where he scaled the AI and data science business. He’s a regular speaker at major data science conferences recently gave a talk on “Sequence Embeddings for Prediction Using Deep Learning” at GIDS. He’s the author of a book on NLP with Apress and currently authoring couple more books with Packt on deep learning and next gen NLP. He is also a visiting faculty (industry expert) at few of the top universities in India. In his spare time, he likes to read, write, code, and help aspiring data scientists.

Sessions

13:3017:00 Tuesday, 15 October 2019
Location: Buckingham Room - Palace Suite
pramod singh (Walmart Labs ), Akshay Kulkarni (Publicis Sapient)
Average rating: **...
(2.40, 10 ratings)
An estimated 80% of data generated is an unstructured format, such as text, an image, audio, or video. Vijay Srinivas Agneeswaran, Pramod Singh, and Akshay Kulkarni explore how to create a language model that generates natural language text by implementing and forming a recurrent neural network and attention networks built on top of TensorFlow 2.0. Read more.

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