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Dr. Prosenjit Banerjee

Director of Machine Vision Research

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Dr. Prosenjit Banerjee is the Principal Data Scientist at Fractal, leading innovative teams in computer vision and deep learning. With over 16 years of experience in academic research and industrial R&D, he oversees research and consultancy in deep learning, computer vision, edge computing, and their applications across various domains. 

Dr. Banerjee holds a Ph.D. from IIT Kharagpur and has published extensively in top-tier conferences and journals. Before Fractal, he led advanced research and innovation teams at OPLUS and OnePlus, focusing on cutting-edge solutions for smartphone features. He excels in mentoring and fostering a culture of innovation and collaboration.

Dive deep into the world of AI's latest foundation models in this informative one-hour session. We'll explore the intricacies of large multimodal models (LMMs), their emergent applications, and how they revolutionize content generation. Discover the role of a mixture of experts in enhancing AI capabilities and envision the future of AI agents working alongside these powerful models.

The session covers large multimodal models (LMMs), starting with an introduction to multimodal learning and the architecture of LMMs. It highlights the importance of integrating multiple data types, explores various application scenarios, and discusses evaluation metrics and benchmarks. The session also delves into fine-grained grounding, the mixture of experts' approaches, AI content generation aligned with human intentions, and the role of AI agents in interpreting and interacting with LMMs, with real-world examples throughout.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

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